Antoine Cornuejols

An Exploration into Incremental Learning : the INFLUENCE system

Although some recent works are concerned with the incremental acquisition of knowledge, these have often been conceived as modifications of existing algorithms or models added afterwards, not as investigations in their own right in a central problem of learning : how is a system to learn incrementally through experiences and time, and how is it to make do with limited computational and memory ressources ?

In other words, how is the temporal and dynamic dimension of learning to be taken into account ?

This paper proposes a clearcut and operational definition of "incremental learning" as opposed to "direct learning" and "sequential learning". In particular this definition leads to the consideration of the "best education" of a learning system, that is how to best organize available data so as to guide the system towards a desired functionality.

A system called INFLUENCE is then described that adapts incrementally its model of the world. An important characteristics of this system that is both significant and novel, is that it is naturally functionning out of equilibrium, allowing it to respond in real time to the environment, contrary to the all-pervasive quasistatic systems, but giving rise to new problems on the best learning rate.

In that way, INFLUENCE allows experiments encompassing the whole spectrum of problems (ordering of data and rate of presentation) specific to a learning process that takes place over time. Results obtained so far are reported in the last section of this paper, and the conclusion underlines lessons and perspectives for this research.

Motivation

Consider your learning of the language Lisp, or the way you came to distinguish between wines or music styles, look at a child developing and growing in its grasp of the world, remember the way your computer science department had to go to master, maintain and adapt to different hardware and software configurations over time. It is apparent that most realistic learning tasks are characterized by their unfolding over a certain duration, which, often, extend over the entire life of the learning system. Yet, except for the pioneering works of the earlier cyberneticians and connectionists and of some precursors in symbolic learning, the vast majority of the research efforts has been concerned "only" with "direct learning". Direct, because as in direct access memory, the whole set of relevant data is supposed to be available at once, with the additional assumption that the system is able to grasp its totality in a single learning step. This state of affair is quite understandable in view of the difficulties faced even in this simplified situation. However, we feel that on the one hand too a great ignorance of the specific problems imposed by incremental learning could lead to a prejudicial forgetting for reasons that will be soon developed, and on the other hand that we can start interesting investigations in this domain now.

Most current research on learning (empirical or explanation-based) still relies implicitly on three assumptions:

-unlimited or at least sufficient computing power to treat all data at once -unlimited memory ressources that allow to keep data in their original form for treatment -all pertinent data available for the learning task All three assumptions are doomed to be dropped in face of realistic learning tasks where computing power is limited compared to the wealth of data and the time requirements, memory space is limited conducting to pre-processing stages and some form of data compression hence forgettings, and decisions and actions have to be taken before all pertinent data are available or can be analysed. Therefore a capacity for incremental learning is necessary. But what really is incremental learning, and is it fundamentally different from direct learning, or can we find some transformation that make them equivalent ?

The naive approach is to call incremental any learning process where the training data are provided in a sequential manner to the learning system. Obviously this definition cannot found a distinction between learning types since one can always allow the system to store all data before starting to process them. A more sophisticated approach is to consider systems where data are processed and their raw form forgotten as they become available as incremental learners. I have shown elsewhere (Cornuéjols, 1989) that this distinction is still insufficient since one can exhibit simple learning systems where data are considered one at a time and not kept in memory in their explicit form, and still producing a behaviour that is quite undistinguishable from a system that would consider all data at once 1 . I call this type of learning sequential. A stronger restriction is thus required, a restriction that takes into account differences in behaviours. I therefore propose the following definition:

A learning process is incremental when the "history" of the training period is influential on the function reached by the system.

That means, for instance, that different orders of presentation of the same training data can produce different functionalities of the training system.

Using this vocabulary, we get the following picture of the possible learning situations : direct learning incremental learning

sequential learning

Figure 1: There are fundamentally two types of learning process, the direct one, and the incremental one where teaching history has an impact on performance.

This last one covers most of the learning situations. Sequential learning 1 Besides, who would be responsible for judging if the form under which information is kept in the system is fundamentally different from the form of the raw data ? Systems are to become increasingly opaque to the observation of their internal workings as shown for instance with connectionist systems.

can be of either one type or the other depending on the commutativity of the messages received from the environment or the teacher.

Most learning tasks certainly implies sequential learning as we have seen. Now, one immediate question is whether it is possible to make any incremental learning process a direct one (where order of data presentation does not affect performance), and, if yes, under which conditions. A preliminary theoretical study of this question can be found in (Cornuéjols, 1989). It shows that this transformation, when possible, imposes very stringent conditions on the types of messages coming from the environment, and on the amount of information that has to be kept by the system. A second question, of practical interest, is how to determine the best, or at least a good, teaching strategy in case of sequential learning. This question covers the choice of the data to be presented to the system as well as the order of this presentation. This is akin to the controllability problem in mathematical system theory, but it is even more difficult to formalize and to solve in Artificial Intelligence. We can try however to get a grasp on this problem by experimenting with systems that are sensitive to their "education trajectory". This is, in part, what we have done with the system INFLUENCE described in the following.

Previous related works

Very few works exist on the specific problem of optimizing an Artificial Intelligence system education. One can cite the paper of (Mac Gregor, 1988) which deals more particularly with monotonic learning tasks where no revision of interpretation has to be done. Strangely, people that study connectionist systems or Genetic Algorithms, which are intrinsically alike dynamical systems, have not, to the best of my knowledge, delved (yet) into inquiries on the temporal aspect of learning. Likewise, but for more understandable reasons, people working on non-monotonic logics and reason maintenance systems have ignored this question. A slight departure from these logicist studies is [START_REF] Elgot-Drapkin | Life on a Desert Island: Ongoing Work on Real-Time Reasoning[END_REF] where the authors deal with systems that have limited computing and memory ressources and must cope with the world in real-time, however they do not touch the problem of continuous learning.

Incremental learning with INFLUENCE

INFLUENCE is a system that was designed to experiment a different approach to knowledge maintenance from the more classical TMS like ones. Basically, the model relies on "spontaneous" changes in the knowledge base under local control when new information is added or under the pressure of internal perturbations. A good image is one of a chemical molecule that re-organizes its configuration in face of new constraints without the help of an external supervisor. This allows us to see the process of knowledge acquisition more as an adaptive one than one of creation of a knowledge base from scratch, and leads naturally to the study of sequential and incremental learning.

The domain

One domain of application that seems particularly appropriate for the study of the problems of incrementally building a model of the world is Natural Language Processing.

Indeed, it presents us with the typical constraints one encounters in incremental learning.

Data are provided and analysed sequentially. They can be ambiguous and their interpretation necessitates to take into account the context, that is the informations currently known to the system. The integration of these data to the memory implies a change in their representation. And, finally, the global interpretation or model of the world rests generally on incomplete and uncertain informations and therefore on hypothetical inferences that may be contradicted by subsequent data. Hence the system must be able to detect resulting inconsistencies and correct them in some way. All these problems are to be found in one form or another in any sequential learning system. This is due to the difference of description between the two levels: global and local. In part for this reason, and in part because there existed already a way of representing knowledge in AI with objects that could be made autonomous, we choose to found our system on frame like entities.

The

There are three advantages to this choice. First, the gap between the description of the behaviour of the global system and the description of the local representation is leveled out.

Second, frames can conveniently be considered as self-contained agents. Third, it is easy to cast existing frame-based Natural Language Processing systems (such as BORIS [START_REF] Michael | In-Depth Understanding[END_REF])) into the INFLUENCE framework.

These considerations are important since they determine somewhat the type of learning realised. Here, the system is supposed to be endowed from the start with the adequate concepts necessary to perceive and interpret the environment, these correspond to the available library of frames and the system does not have to learn them, however the task is to progressively build a model of the world (a kind of mega-concept) in term of which incoming messages can make sense, and decisions and actions can be made. We call this type of learning episodic or contextual learning. A system that learns to make diagnostics in certain types of situations falls into this category.

In frame-based systems, interpreting, understanding and inferring mean instantiating schemas or frames and establishing links between them. Conversely, retrieving informations mean identifying some relevant structure in memory and "reading" from there the appropriate schemes and links. This is what is routinely offered as facilities in existing frame-based systems. These systems however are generally unable to allow knowledge revision except in some cases under the supervision of an heavy supervisory system à la TMS [START_REF] Jon | A Truth Maintenance System[END_REF][START_REF] Richard | When expectation fails: Toward a self-correcting inference system[END_REF][START_REF] Paul | Reasons for beliefs in understanding: applications of non-monotonic dependencies to story processing[END_REF] and with the need to have kept most informations from the raw data in the first place, two unpleasant requirements.

Yet, it suffices to destroy some links and frames and to construct new ones to change the content of the memory. This is what does INFLUENCE. In this model, the knowledge base is conceived as a self-organized organism where the links are autonomous agents that can decide for themselves if and when to detach from their current target to look for a new one.

This process is guided by local informations on the adequation between constraints inscribed in the slot corresponding to the link and the current target. The better this adequation is, the stronger is the corresponding link and the more unlikely is its detachment from the target. The uncertainties of the system are thus translated into the instabilities of the links in memory.

Meanwhile, the choice of a new target is determined by the respective "influences" or activities of the potential targets. These influence factors represent the importance of the attached scheme or concept in the current context. They are directly depending on the connections maintained by each frame in memory. Specifically, the use of these coefficients allows to attract links preferably towards those concepts that are the most central in the current situation rather than towards more peripheral ones. This implements in a way the parcimony principle that dictates to prefer simple and economical explanatory schemes rather than complicated ones.

Within this framework, possible interpretations of a given set of informations are alike local equilibrium states corresponding to different basins of attraction. Revision or modifications to the current model of the world occur when perturbations, either external (tied to incoming data) or internal (due to the instabilities of the uncertain links) push the system towards other attractors. The traditional phases of inconsistency detection and belief revision are thus tightly mixed, and cannot be in fact distinguished.

Typically, at any given time, there are some links in the memory network that are "uncommitted" from their current targets and are looking for new ones. The probability that any one of them attach to a given target depends on the relative influence of the latter. If the current network corresponds to a satisfactory state of the memory, links are rather stable (local adequations are good) and current targets have large influences making high the probability that the rare uncommitted links re-attach to the same target, leaving the memory unchanged. The network is then quite stable. On the other hand, if the current memory model is unsatisfactory, links tend to be unstable, and frames that were previously rather poorly influent may get a chance to become more central yielding in turn other modifications and leading to a new organization of the network and to a change of the overall interpretation.

The same may happen when new data arrive. Either the frame representation of these new informations fits well within the existing network resulting in an extension of it and a corresponding elaboration of the current world model, or they do not fit, leading to one of two possible outcomes. One is that the new data are simply forgotten because of their beeing not significant enough to maintain themselves in memory in isolation, the other is that a more or less important re-organization of the memory has to take place to accomodate the new data.

For a casual observer, the whole process of data integration into memory is quite analogous to the way a chemical molecule re-arranges spontaneously its structure when coping with new stresses.

One noteworthy point besides is that the system never reach rest except in the rare cases when memory is empty (an hopefully extreme situation indeed), or when all links represent perfectly certain assertions and are thus totally stable. That means in particular that the dynamics of the system must be taken into account when controlling interactions with the environment. If incoming data are too sparse, the risk is high that too much forgetting takes place between incoming messages to maintain a faithful model of the world. If, on the other hand, they are coming too fast, the risk exists that the system be overwhelmed and not be able to accomodate for all of them. This is at least the intuitive view.

To sum up, because INFLUENCE is a model of adaptive learning taking place over time, and because its out of equilibrium mode of functionning distinguishes it from classical quasi-static system where one quietly waits for the system reaching a stable state, one can expect interesting experiments and results about all aspects of incremental learning.

The next section provides a glimpse of such experiments.

Experiments

Numerous and various experiments have been realized with the INFLUENCE3 system applied to Natural Language Processing tasks. The typical protocol was the following:

various entries corresponding to a given text were translated by hand4 under the form of their canonical representation in frame-based structures, and then these fragments of semantic networks were entered into the system in various orders or/and various rates (with respect to the internal inferences cycles of the system).

One text for instance read as follow :

"When John learn t in th e news that h e had won the lo ttery, he bough t immediately an airplan e ti cket for In don esia. H e was very exci ted. He rush ed to make his luggages, and l eft his appartmen t withou t closing th e gas. Three days la ter in Dja karta, h e learn t that two mafi osi had died in th e explosion of his appa rtment. He was reli eved."

Note that this text presents an interesting property since most of us will at first think that John went abroad for vacation before choosing, upon reading the last sentences, the less mundane interpretation that he was fleeing the mafia. The questions are, will the system be able to do the same, and will its understanding process be more involved than if the data had been in a different order with the last items of informations appearing first ?

One would thus compare the learning and interpretation processes when in situation 1:

1st input : "When John l earn t in th e n ewspa per" 2nd input : "he ha d won th e lo ttery" 3rd input : "he bough t an airplan e ti cket" 4th input : "he was very exci ted" 5th input : "he made his luggage" 6th input : "he l eft his appa rtment withou t closing th e gas" 7th input : "John kn ew that two mafiosi were looking for hi m" 8th input : "John l earn t th ey ha d di ed in his a ppartmen t ex plosion"

and in situation 2 where the last two items are entered in the system before the others.

In other experiments, one would vary the time delays between inputs, say 0 cycle (all data provided at once), 10 cycles, 20, 50 and so on, and with various parameters combinations that limited space prevent us from discussing here.

The first interesting result is that the system is indeed able to change correctly its interpretation of the world when subsequent data are in conflict with the current one, as for instance when input 7 and 8 are taken into account in situation 1 above.

The following picture gives an idea of the progress of the learning task as inputs are provided to the system (x axis). The y axis represents the completion (in a crude approximation) of the current memory model measured in percentage against the desired final model. One can observe that the completion measure increases with each new input, but also that there is a jump when inputs 7 and 8 are provided. Note too that the curve is going up between inputs as well, reflecting the fact that the system continues to make inferences and to build its memory network. As seems reasonable afterwards, with a system presenting good abilities for reinterpretation the effect of the differing orders of input streams were not as strong as expected. Overall, with different sets of data and after numerous experiments, it was found that learning and interpreting was difficult mostly in those cases where data with high but ambiguous information content where given at first without rapidly providing keys that would allow disambiguation. Otherwise it was found that the best results in terms of learning speed and performance were attained when giving first key informations followed by complementary ones that allow the system to develop its memory network and to confort its first inferences.

Of course, these observations would deserve to be more formalised. The information content of each piece of data was measured with respect to the number of frames and links necessary to represent them, and in term of their likeliness. For instance you can arrange your background knowledge (expressed in the form of the frames and the procedures attached to their slots) so that it is likely that you learn on the radio that there was a coup d'état somewhere in the world, but unlikely that you learn on the same radio that your reseacher salary has been raised. In fact, in this last case, the information content depends also on the context. Whether you are yourself a chief of state or a reseacher can totally change the impact of the data on your view of the world. This is why the learning system itself should be the judge in the last resort of what is informative or not. This has been done using some simple heuristics in INFLUENCE, but space limitations prevent us to elaborate on this here.

As for the effects of the rate of input data on the learning process, results show that the highest rates are the best ones in terms of the correctness and completion of the final memory model obtained. On the other hand, intermediate rates appear better with respect to the speed of learning each individual data. Lowest rates lead to irremediable loss of informations (note that the forgetting phenomenon is interesting in itself: unimportant details are the first to disappear).

These results are somewhat surprising since one would expect that giving all data at once would result into great difficulties for the system to analyze them all. It must be noted however that the INFLUENCE model is misleading in some sense with respect to this problem because it implements massive parallelism and does not have, in principle, limited computing ressources. This is why the system can absorb, and reflect upon, large batches of data.

In this way direct learning seems better than incremental one. But as we have seen, even if this rule was to be confirmed, realistic learning situations would prevent its use.

Conclusion

As the uses of Artificial Intelligence systems increasingly involve more automatic learning extending over the life of the systems from incomplete and uncertain informations, sequential learning (where data are considered as they come) and adaptation of the knowledge bases will become the norm rather than the exception. It thus happends that for all but some very constrained cases, sequential learning implies a sensitivity on the "history" of the learning process, a phenomenon I call incremental learning. It is then crucial to ask oneself how to optimize the education of such systems so as to reach the desired performance levels.

Likewise, the growing complexity of Artificial Intelligence systems and of the tasks to them devolved, will render untenable the assumption of quasi-staticity required nowadays in their operating. Future systems will have to be able to function out of equilibrium. Now, how are we to control and guide such systems ? Answers to this question remain largely in the realms of fantasy.

We advocate that more research is needed, and urgently needed, to explore the temporal or dynamic dimension of learning. Large theoretical studies are necessary. They will undoubtly be difficult and involved. In the meantime, experimental studies can prove themselves worthwhile.

  The system INFLUENCE was designed to have a stab at such an inquiry. Results obtained so far are still modest (I think that one reason is that there are too many problems in dealing with open domains like Natural Language Processing, particularly at the level of knowledge representation. Further experiments should be realised in more constrained and better formalised domains like fault diagnosis). I hope however that these experiments point towards interesting directions.

  The problem is to be able to bridge the gap between what we would like the system to realize and what is realized at the level of the cells. In Physics or in Chemistry this is still an open problem in its generality, in Artificial Intelligence it appears sometime under the guise of the credit assignment problem, and except for very constrained systems (such as back-propagation schemes in neural networks) it is immensely difficult to solve.

	exhibit quite interesting self-organized and adaptive behaviours when immersed in
	environments rich in flows of energy or matter. How can one use this metaphor in
	information systems ?
	It would be nice to take a collection of very primitive interacting devices, such as cells in
	a cellular automaton, and let them self-organized when confronted with a given
	environment.
	INFLUENCE system 2
	INFLUENCE is a realisation for artificial intelligence purposes of a dynamical system
	builded out of numerous basic autonomous entities. It is known that these systems can

This section intends to give only a brief overview of the INFLUENCE system, a detailed account of the system and of the corresponding experiments can be found in(Cornuéjols, 1989) and (Cornuéjols, 1987).

INFLUENCE has been implemented with Allegro Common Lisp on a Mac IIx microcomputer.

 4 But existing parsers could be used as well with minor modifications so as to attribute a certainty factor to each link depending on syntactical constraints and heuristics used during the parsing.


Jean -Pierre Cassou

Antoine Cornuejols

Arcanset Group

Bd R Poincarré

Garches Et

Statistical filtering of motion field from image sequences

Techniques for the estimation of the motion vector field from image sequences may be subdivided into two main different approaches. Either the motion field is calculated from tracking shape or feature correspondences between images, or it is estimated using the optical flow vector field method and its variations.

The latter approach is very alluring because it doesn't require the previous identification of remarkable points in the images, but so far it has stumbled over the complexity of interpreting, i.e. solving, the gigantic set of interdependent constraints generated. Furthermore, it has proven difficult to introduce and express in such a framework the notion of model of what is looked for in the images.

We treat both of these problems in this paper. First, we identify the interpretation of an optical flow vector field, obtained from a first order Taylor approximation of the spatial variation of the gray gradient [ Schunk85 ], to a complex optimization problem. And we give it a form which make it amenable to new techniques based on energy minimization recently introduced [Kirk83, Carn85]. Second we introduce the notion of models of what is of interest in the images. Particularly, it was found interesting to focus on three particular types of motion: uniform motion, rotation and contraction/ dilatation. Together with a definition of the types of shapes to be looked for, this provides a parametric model of the patterns that are to be detected or extracted in the vector field.

We then are able to run a simulated annealing process which tends to minimize the difference between the energy asssociated with the "observed" vector field , and the energy associated with a vector field that would result from the parametric model. At the final equilibrium stage, this yields the optimal parameters of the model that would produce the noisy image sequence observed, and therefore provides the user with a high level description of the image sequence in terms of the phenomena he/she is looking for.

We illustrate this method with the detection of objects in uniform motion in a sequence of images.

Problems of performances of this scheme and perspectives for further improvements are also addressed in the course of this paper.

The problem of identifying moving objects.

The problem we study in this paper is, very classically, the identification of objects in motion through an image sequence. It implies both the description of the objects and the characterization of their motion. This is not a trivial task because in general there are infinitely many two-dimensional velocity fields that are consistent with the changing image, and additional constraints, based on assumptions about the physical world, must be made.

We chose to take as a constraint that the image sequence resulted from a parametric model involving solids in translation, rotation or compression/dilatation. And, considering it as an optimization problem, we solved it with a recently developped technique: Simulated Annealing.

The classical approaches and previous works.

The analysis of image sequences to detect moving objects in dynamical scenes is full of applications, for instance in robotics, medicine, transports, or meteorology, and has been an active on-going research axis for a long time. Several approaches have been tried.

Leaving aside the so-called global approaches [Nagel,1986] that postulate that the differences between two frames in a image sequence can be described by a function depending only on a few parameters, the most widely used techniques are local. They emphasize the study of differences at the level of the pixels between two images. Among them one approach consists in the determination and extraction of "change regions" by establishing first a parameterized description of structural gray value perceptions and then applying statistical tests to isolate the significant regions of variations. This approach meets some difficulties in exploiting the resulting map and in disentangling the factors that may have contributed to the detected changes without calling for highly application-specific knowledge. There remains two main approaches.

One is to analyse separately the images of the sequence using classical segmentation techniques in order to isolate the elementary patterns. Then one tries to track localisable features through the images and to measure their displacement. This method works well for 2D images of rigid objects but is prone to failure in case of 3D scenes of plastic patterns.

The other attempts to directly evaluate the displacement vector u from the spatio-temporal gray values I(x,y,t) available from at least two images. Actually, only the optical flow, which denotes the image plane shift of the gray structure between the images, can be derived from this information. This approach has been investigated by many authors and is more thoroughly described in the following. It is promising because it is in essence independant of any application-specific information and may soon justify the implementation of special purpose processing architectures. Its use requires however some additional assumptions which are the basis of the differences between the authors. We ourselves emphasize certain hypotheses that seem natural, and, above all, demonstrate the use of a new computational procedure to solve the corresponding problem. Besides, it is noteworthy that this computational tool may be applied almost as well to other optical flow models.

Statistical filtering of motion field.

Section 2 gave an overview of the different possible paths to the estimation of motion in an image sequence, mainly tracking feature correspondences, or interpreting the optical flow vector field. The approach taken in this work belongs to the second category. We consider that it is more easy and natural to extract the information concerning movement from the low-level and local data corresponding to spatial and time gradients in the image sequence, rather than by involving high level expectations about the scene and complex non-local computations. But our own approach doesn't preclude the possibility of providing the vision system with a model of what is looked for in an image. This is actually, as we shall see, the basis of the novelty of our own approach compared to the one of [Horn & Schunk,1981]. However the model lays on domain-independent constraints and the computations remain completely local giving rise to interesting possibilities for rapid processing.

A brief account of the optical flow concept is first given in order to make this paper self-contained and to explain the [Horn & Schunk,1981] assumptions and method. Then the simulated annealing technique is described together with our implementation for the analysis of image sequences. And finally the results obtained using this method, and plans for future improvements are given.

The optical flow concept.

The idea of optical flow rests on the constatation that a real time vision system, such as biological ones, observes images that vary essentially continuously in their characteristics. This leads to an interesting method of motion perception based on local comparison of brightness.

Let characterize each point (x,y) at time t by its brightness I(x,y,t). If during the period dt, the point has moved in the direction (dx,dy) then we get: I(x,y,t) = I(x+dx, y+dy, t+dt) Then, expanding the right hand side of this equation using a first order Taylor expansion:

I(x,y,t) = I(x,y,t) + --------- !I !x dx + --------- !I !y dy + ------- !I !t
dt which yields:

-I t = I x v x + I y v y (3.1-1)

That means that the time rate of change in intensity of the image is the product of the spatial rate of change of the brightness region by the velocity of that region.

The equation (3.1-1) constrains the velocity (v x ,v y ) to lie on the line it expresses. As [Horn & Schunk,1981] note, "optical flow cannot be computed locally since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed." [Horn & Schunk,1981] postulate that the spatial variation of the optical flow components should be as small as compatible with the system of Euler-Lagrange partial differential equations associated with the optimization problem specified by the equation:

! ! ! ! " # ! ! ! ! " # dx dy ([$I % && T • u % && + I t ] 2 + ' 2 [ ] u x 2 +u y 2 +v x 2 +v y 2 ) % && minimum
They then employ the iterative Gauss-Siedel method to obtain an approximation to the system expressed above.

With our technique, we are able to get rid of the smoothness hypothesis and to replace it with a natural spatial constraint: pixels which are part of the same object should have the same velocity. We then give a very general model of neighbourhood that can cover and segment the image into objects of different velocities. The segmentation process and the corresponding determination of the velocity characteristics are obtained very simply using a technique derived from statistical physics, and recently applied to several optimization problems: simulated annealing.

Simulated annealing and image sequences.

The method of simulated annealing is a technique that has recently attracted significant attention as suitable for optimization problems of very large scale. Its underlying principle rests on the observation of Nature's strategy to reach states of minimum energy for slowly cooled systems. Thus, liquid metal sufficiently slowly cooled reach a quasi crystalline structure corresponding to their minimum energy state. To do so Nature employs a minimization algorithm based on the so-called Boltzmann probability distribution, Prob(E) ~ e (-E/kT) This equation expresses the idea that a system in thermal equilibrium at temperature T has its energy probabilistically distributed among all different energy states E. In other words a system described by its degrees of freedom x i has all of its possible configurations {x i } weighted by their respective probability factor, e (-E({xi})/kT) , where E({x i }) is the energy of the configuration. The "miracle" is that at low temperatures, the otherwise extremely rare ground state configurations among all the possible configurations, are the ones that dominate the Boltzmann distribution. However low temperature is not a sufficient condition for finding ground states of a system, if it imposed too rapidly, before the system can in a way make a rough exploration of the phase space, the system is likely to get trapped in a local minimum configuration. In practical contexts, a careful annealing is required.

In 1953, Metropolis et al. [Metropolis,1951] first incorporated this kind of process into numerical calculations. The idea is that, accordingly to thermodynamics, a system sometimes goes uphill as well as downhill; but the lower the temperature, the less likely is any significant uphill excursion. each step of the calculation the system can make a choice between several options, if, say, it considers an option that leads to a change DE in the energy, it will actually take it and change the state of the system with a probability p=e (-DE/kT) . Notice that if DE<0, i.e. this option leads to a decrease in energy, the probability is greater than unity, in such cases the change is arbitrarily assigned a probabilty p=1, that is the system always takes such an option. This general scheme, of always taking a downhill step while sometimes taking an uphill step, is known as the Metropolis algorithm. By repeating this step many times with a given temperature T, one simulates the thermal motion of atoms in contact with a heat bath at temperature T. The whole optimization process involves lowering the temperature by slow stages until the system "freezes" and no further changes occur.

Provided with this wonderful "natural" mechanism the art of the researcher is to provide the energy function describing the system together with the options opened to the system, its possible configurations and the control parameter T.

In this research we applied simulated annealing to the optimization problem related to the finding of motion field from the optical flow obtained from image sequences. In this scheme, the states of low energy correspond to the "ideal" optical flow stemming from some typical movements of objects in the scene. For instance, we first tried to identify objects in uniform motion. Given this model of what is of interest, it is possible to derive a corresponding energy function that is minimum when the state of the system segments the scene into regions of uniform speed. In that way, starting from any initial state (segmentation with attached speeds), and given noisy data, the algorithm will converge toward the ideal scene that would have produced such data.

The constraints that allow us then to extract the necessary information from the optical flow are a spatial constraint: for instance, the pixels of an object are adjacent and have the same velocity; and a constraint on the type of velocity field that are looked for: for instance, uniform motion, rotation, contraction/dilatation with respect to a fixed point. This guides the determination of an appropriate energy function.

As an example we give here an illustration of the simulated annealing process applied to the determination of objects in uniform motion from two images.

The images are square ones. They are multi-leveled gray (256 levels in our images). And there is no assumption on the scene observed other than the objects are connected (this can also be changed easily), and their respective motions are uniform. There can be a large amount of noise, and that was actually the case in the images we analyzed.

The first point is to see what that means for a collection of points to be in uniform motion (or for that matter in any other type of motion as soon as it is well identified). The optical flow hypothesis allows us, as we saw in section 2, to draw the line on which the velocity (u i ,v i ) of a point i must lie. In the ideal case, then, a collection of points animated with the same uniform speed, should have their respective lines intersecting at a unique place in the (u,v) plane corresponding to their speed (given that all these lines are not a single one which is highly unlikely). Finding this place or speed is equivalent to finding the parameters u and v that minimize the following systems of equations, (3.2-1) corresponding to a L 1 norm, and (3.2-2) to a L 2 norm. y i ,t)

! x u i + -------------------------------------- !I(x i , y i ,t) ! y v i + -------------------------------------- !I(x i , y i ,t) ! t (3.2-1) ! i " # $ % & ' -------------------------------------- !I(x i , y i ,t) ! x u i + -------------------------------------- !I(x i , y i ,t) ! y v i + -------------------------------------- !I(x i , y i ,t) ! t 2 (3.2-2)
A possible configuration of the system consists therefore in a segmentation of the image at time t, for which the corresponding global energy is the sum of the energies of each segment calculated with the formula (3.2-1) or (3.2-2). For instance, applying the equation (3.2-1) to look for the velocity (u s ,v s ) minimizing the energy of the segment s, we find that the global energy is given by (3.2-3): --------------------------------------!

I(x i , y i ,t) ! x u i + -------------------------------------- !I(x i , y i ,t) ! y v i + -------------------------------------- !I(x i , y i ,t) ! t
At each step of the algorithm, the system can either (1): split an existing segment or region into two pieces, that therefore can have different values (u,v) minimizing their respective energy, or (2): replace a segment by the union of itself and one of its neighbour, yielding a new value (u,v) for all the points of this new region. Each time the system consider an option, SPLIT or MERGE, it computes the attached change in energy DE and acts accordingly to the decision of an oracle computing the expression e (-DE/kT) . Of course, if the energy was exactly the one given by expression (3.2-3), the option SPLIT would never be rejected, because even in the worst case where the two new regions were in fact part of the same object and having therefore the same velocity, the corresponding DE would simply be 0. It is thus necessary to add a coefficient to (3.2-3) penalizing great numbers of segments in the scene. That way a split would be profitable to the system only if it brings effectively a better segmentation of the image. Accordingly one obtains a definition of E which is the sum of the energy given in (3.2-3) and of the term: κn s , where n s denotes the number of segments or objects in the scene.

Conclusion.

We have cast the classical problem of motion identification into an optimization framework well suited for the method of Simulated Annealing. Accordingly the expression of an energy function was given, which minimum corresponds to the ideal parametric model associated with the image sequence observed.

This new technique, in addition to solve elegantly the problem, allows one to "play" with variations on the formulation of the model, for instance the type of energy function to minimize or the kind of possible spatial re-arrangements giving a segmentation of the image. But above all, one of the main advantages of this approach is to allow the user to define and then obtain high-level descriptions of the phenomenon under study, e.g. in terms of polygons or whirlpools.

It was experimented with success on uniform motion cases using an "augmented" PC-AT with FORTRAN programs. More experiments are planned to determine the efficiency of this method on complex scenes. Its robustness to noise is inherently very large. Schemes to improve the computing speed are projected.

Fig. 3

 3 Fig. 3.1-1: Constraints on the velocity of the point (x,y)


D4 Wp4

Dr C L Sansom
email: c.l.sansom@cranfield.ac.uk

Dr Kumar Patchigolla
email: k.patchigolla@cranfield.ac.uk

Executive Summary

This report forms WASCOP deliverable D4.2 entitled "Water Management Guide". The Report arises from the work done in order to complete Task 4.1, and has been completed on schedule in M16 (April 2017). The Report (and deliverable D4.2) has "Public" dissemination level.

The work in Task 4.1 was led by Cranfield University, with significant contributions from CIEMAT-PSA, and additional contributions from MASEN.

The main aim of Task 4.1 was to establish and analyze the current CSP plant water consumption, and predict the reduction in water consumption that could occur if novel cooling and cleaning technologies were to be adopted. As a result, the task was aligned around two main sub-tasks, each with its own deliverable in Month 16 (April 2017):

• Publish a public document entitled "CSP Plant Water Management Guide". This document (Deliverable D4.2) provides a description of the current water consumption in CSP plants and outlines the reductions that can be achieved with the adoption of a number of new technologies in cleaning and cooling.

• Publish a confidential report entitled "Analysis of water consumption in CSP plants". This report (Deliverable D4.1) provides a detailed model of the water consumption in CSP plants, and provides a simple means of predicting the reductions in water consumption that can be achieved for a range of novel technological improvements in the cooling and cleaning systems (including those developed under WASCOP).

This current document includes Deliverable D4.2, The Water Management Guide. Deliverable 4.1 is published in a separate document.

The information contained in this document was assembled by the WASCOP partners, with additional input from the following sources:

• Joint WASCOP/MinWaterCSP workshop on "CSP plant water consumption" at CIEMAT-PSA on November 29 th 2016, attended by 28 external delegates including plant owners, plant operators, plant maintenance and other service providers, and organized by the WASCOP WP4 participants.

• An on-line questionnaire, sent to a number of CSP plant stakeholders in March 2017.

• A Cranfield University MSc group project on "CSP plant water consumption", School of Water, Energy, and Environment, Feb-Apr 2017.

List of Figures/Tables

Introduction

Today more than ever technologies that produce green energy are playing a growing role in meeting the world's energy demands. CSP is most effective around the sun-belt where DNI levels are at their highest. The main challenge of building CSP plants in these areas are the scarcity of water which is used for cleaning and cooling. According to a survey of plant operators, the typical average water consumption of a 50 MWe wet cooled parabolic trough plant is around 400,000-500,000 m 3 /year of which 10-20% is used for cleaning and 80-90% for cooling. Therefore, by efficiently managing the water usage the effectiveness of the plants increase. Under the WASCOP project, new technologies are being developed and tested to prevent losses in efficiency while reducing water consumption.

System Description

Cooling

A CSP plant utilizes a conventional steam turbine and differs from a conventional fossil fuel fired plant only in that the thermal input is provided by the solar field. In a steam turbine, water enters the system from the condenser then it is pumped to the boiler where water will be totally transformed into steam. After the expansion in the turbine, a portion of the steam return to water, and the rest is still in a steam stage. In other words, one has a steam with a quality less than 1. To be able to reuse this steam, or water source again, one needs to cool down the steam to return it to the subcooled region. This phenomenon take place in the condenser. Water is then pumped into the system for another cycle.

A steam turbine operates based on the Rankine cycle. The lower the temperature after cooling the higher the efficiency achieved by the turbine. It can therefore be concluded that the cooling system has a great impact on the plant.

There are three different methods for cooling: wet, dry and hybrid. The wet cooling has the highest efficiency among its peers. However, due to the likely location of a CSP plant in an arid site, water is scarce and thus one is required to transport water to the site either by trucks or by pipeline installation. At best, local wells or rivers can be used, but the depletion of these water courses can cause adverse environmental impacts on local communities and local agriculture. Moreover, the cost associated with providing water in this way is very high. A dry cooling system uses air as a medium of cooling. This system is the most efficient when it comes to water usage, however it has an adverse impact on the efficiency of the plant. A hybrid system may reduce water consumption by more than 70% compared to the wet cooling however it can have a significant negative impact on the capital cost of the system.

Cleaning

Large CSP plants require a substantial mirror area, typically in a desert or arid environment. Due to the high DNI in these areas they prove to be ideal locations to harness solar energy. The problem faced are that the mirrors are subjected to dust and sand deposition which reduce the reflectance of the mirrors resulting in a reduction of power output and efficiency. Therefore, the mirrors need to be cleaned frequently to ensure that optimal reflectance is restored. There are three different methods of mirror cleaning, wet, dry and hybrid. Wet cleaning has been found to be the most effective method of cleaning, normally with the use of demineralised water and a brush. However, due to the climate, the ideal CSP plant locations often have a scarcity of water which incur further costs of sourcing the water. In addition, the high quality water required to clean the mirrors significantly increase the O&M costs of the plant. Therefore, much research is being done to quantify the effectiveness of dry and hybrid cleaning solutions to reduce the amount of water usage for cleaning.

Other research to better understand the transportation of dust and other particles are also being conducted in an attempt to mitigate the overall deposition on the mirror surface.

Current Practice

Cleaning

This section describes the current cleaning technologies used in CSP plants. A description of the methods employed and their results will be discussed as well as the environmental and economic impact of these technologies.

Technology

Most CSP plants are located in semi desert areas where the environment is characterized by high temperature variation, soiling ambiences and periodic dust storms. These environmental conditions are very aggressive on the mirror surface as storms of wind and dust particles reach the surface of the mirror and reflectors, decreasing the reflectance of the mirrors and the overall performance of the CSP plant. Wet or hybrid cleaning has been shown to be a more efficient way of cleaning the mirror surface and receiver compared to dry cleaning. Therefore, large amounts of demineralized water are consumed for washing the mirror surface and reflectors. 2 -20% of the water used in CSP plant is used for cleaning operations which significantly increases the cost of operation and maintenance of the CSP plant.

The amount of water used in cleaning the mirrors and receiver tubes varies with the technology used and the frequency of cleaning. A detailed comparison of four different cleaning methods used for reflector washing and receiver tubes has been is described below. The results show the following methods and the amount of water consumed per square meter of the mirror surface.

The traditional high-pressure rig/low-water-volume

The traditional high-pressure rig/low-water-volume method uses the rig to move down one row and returns to clean the adjacent row, see Figure 1, with water delivered at pressure of 3,000 psi (207 bar). The high-pressure/low volume method uses 0.72 litres of demineralized water per square meter of reflector surface. In high-pressure, with rotating head/low water volume rig, water is supplied to the mirror surface by a rotating rig at high pressure, as shown in Figure 2. The pump can supply up to 170.3 litres per minute at a pressure of 3,500 psi (241 bar). Water use is about the same as in the traditional highpressure rig/low-water-volume of around 0.72 litres of demineralized water per square meter of reflector surface. This method employs nozzles which supply water to rows of reflectors simultaneously with a "deluge-type" stream of water to parallel rows of reflectors. This method uses 0.87 litres of demineralized water per square meter of reflector surface, and is shown in Figure 3.

High pressure cleaner vehicle with demineralized water and cleaning brush;

In this method, demineralized water is pressurized to the mirror surface at a pressure range between 30 and 200 bar, at an appropriate water flow rate. The cleaning vehicles have two arms in the case of parabolic-trough collectors to clean upper and lower surfaces of the mirror and additional washing accessories such as soft thin brushes used to enhance the cleaning process. For heliostat cleaning, typically only one arm is needed. This technology uses a low volume of water and has high cleaning efficiency. See Figure 4.

Environmental Aspects

Accessing water is an important challenge to use in CSP plant in desert areas, as available water resources are highly valued by many stakeholders. Water is used for cleaning the mirrors and receiver tubes to maintain their optical properties. Although the water used for cleaning is substantially lower than that used for cooling, it is significantly higher in desert areas where cleaning of the mirrors is carried out more frequently. The frequency of mirror cleaning can take place between every 2-14 days, typically. This does not include exceptional events such as dust storms and heavy rainfall. It also depends on the type of technology used for cleaning the mirror, as more efficient cleaning methods will use lower volumes of water which will impact less on the other stakeholders that use water for different activities in the vicinity of the plant.

Economic Aspects

All current cleaning technologies use water and brushes (sometimes water alone) to retain the optical surface in a suitable cleanliness level at a minimum cost whilst periodically returning the mirrors to their original reflectance and the receiver tube envelops to their original transmittance. Some abrasion of the mirror surface can occur in contact cleaning, but any degradation in these optical properties usually has causes other than mirror cleaning, namely mirror erosion and corrosion and receiver tube envelope anti-reflective coating erosion.

Further research on cleaning technologies which use less water (or no water) include Transparent

Electrodynamic screen (EDS), dry cleaning using mechanical brushes to clean mirror surfaces, airblowers, anti-soiling coatings, dust barriers, soiling detectors, ultrasonic cleaners, and gravity lip systems. These are discussed later. All involve additional cost, but reduce water consumption.

Cleaning Machines

ECILIMP

The company ECILIMP TERMOSOLAR specialises in cleaning Heliostats and parabolic-trough collectors, ensuring optimal cleaning factors for the solar thermal plants where it provides service. ECILIMP TERMOSOLAR machines use rotary brushes with high pressurized water to perform wet mechanical cleaning [START_REF] Ecilimp | ECILIMP manufacturer website[END_REF]. Features of ECILIMP cleaning machine (Figure 5):

• ECILIMP vehicles use high pressure demineralised water and brush cleaning tools • Cleaning speed control : 0 -5 km/h • Chassis: high strength steel, 50% lighter than conventional vehicles.

• Tank: 8000L PRFV tank, shipping container dimension, front cabinet with shutter lock.

• Maximum relative reflectance of about 99.6% obtained with very low water consumptions.

ALBATROS

The ALBATROS cleaning system ensures maximum cleaning efficiency with low water consumption and reduced operator costs. In 2008 a collaboration between Albatros and Abengoa developed a completely new and innovative product for high pressure cleaning vehicles designed for use in solar thermal power plants [START_REF] Group | Albatros group website[END_REF].

SENER -THE PARIS SYSTEM

PARIS is a cleaning system designed, patented, manufactured and commercialized by SENER that performs autonomous and distributed cleaning of Parabolic-Trough collector (PTC) plants, optimizing the cleaning with a minimum of operation costs.

The PARIS vehicles are autonomous, performing an unmanaged cleaning schedule by virtue of its GNC software.

PARIS performs a wet mechanical cleaning process using rotary brushes able to significantly raise the reflectance factor of the mirrors in just one passing. This efficiency allows reduced cleaning frequency as well as minimizing the water and fuel consumption [START_REF] Troughs | PARIS-Autonomus cleaning system for Parabolic Troughs PARIS-Autonomous cleaning system for Parabolic Troughs[END_REF]. Features of PARIS cleaning machine (Figure 8):

• No driver is required.

• The operator performs punctual non-specialized support tasks.

• Minimization of the damage caused by human errors.

• High availability, not limited by the shifts timetable.

• Minimize the risk of damage due to its stopped cleaning.

• Its low weight minimizes the roads' impact and fuel consumption.

• Its dimensions improve its maneuverability.

• High quality of mechanical cleaning with minimum water consumption.

• High cleaning capacity and autonomy.

• Easy scalable solution and robustness to single failure.

• PARIS is designed for night cleaning, not affecting the daily plant production.

FERMUPE

In the cleaning portfolio, Fermupe offers a comprehensive package for cleaning of solar fields.

Fermupe has developed their own range of equipment for cleaning mirrors of solar fields, as well as photovoltaic panels [START_REF]Fermupe Cleaning Machine[END_REF]. • Central cylinders, Parabolic solar thermal cleaning, covering the entire range (contact and non-contact), with cleaning schedules integrated on a single computer (there are interchangeable elements), which cover all the needs of the plant, and leading to corresponding cost savings. • Vehicles for cleaning of heliostats, PV and solar thermal power plants.

ABENGOA

Abengoa Solar, through its specialized partner, provides its own design of cleaning vehicles for heliostats and parabolic trough collectors, use of which result in higher levels of plant efficiencies. Abengoa cleaning trucks are available for tower heliostats and parabolic trough cleaning [START_REF]Abengoa Cleaning Machine[END_REF]. Features of ABENGOA cleaning machine (Figure 10):

• Interchangeable cleaning tools: high pressure and brushing.

• Cleaning speed control: 0 -5 km/h.

• Chassis: high strength steel, 50% lighter than conventional vehicles.

• Tank :9000 litres PRFV tank, shipping container dimension, front cabinet with shutter lock • Crane: hydraulic canalization by rigid tubes.

• Oleo hydraulic circuit: pumps next to the PTO (Power tank off), detachable pump clutch.

• Electricity and electronics: standardized distribution panel, replenishable control box with joystick, extra sensors (temperature, pressure, flow, power night LEDs, ultrasonic sensors)

Cooling

In CSP plants, the main consumption of water occurs in the cooling system which accounts for 80-90% of the total water consumption. A description of the methods employed and their effectiveness will be discussed as well as the environmental and economic impact of these technologies [7].

Technology

Four main technologies have been identified as current practice suitable for Rankine cycle based CSP plants: two for a water-cooled condenser (WCC) system and two for an air-cooled condenser (ACC) system. The first WCC system is the once-through WCC system described in Figure 11 [7]. This condenser uses water to cool steam via indirect contact. Cold cooling water is directly extracted from the sea (or other reservoir) and all of heated cooling water is discharged into the source.

The second one, and the most common for plants near a river or lake, is the circulating evaporative WCC system described in the Figure 12 [7]. The system extracts the cooling water from a river or a lake, but instead of rejecting the heated water into the source, it is rejected in the air by evaporation in a cooling tower. The cooling tower needs a regular blowdown and consumes a considerable amount of water [7].

To meet the cooling needs of a CSP plant and the water scarcity of the area where these plants are usually located, two ACC systems have been developed [7].

The first one is the Direct ACC system described in the Figure 13. Steam from the steam turbine is carried to the A-framed condenser. The condenser is composed of finned tube bundles which are cooled by external ambient air. All the existing direct ACC systems use mechanical draft which is more reliable (less sensitive to backpressure fluctuation and high wind) and requires less operational intervention than a natural draft direct ACC. The steam circulates via the finned tube heat exchangers from the top to the bottom, where the fans are located, of the condenser. Then, the condensate steam is drained to be reinjected into the cycle.

It has been reported that direct ACC performs well at ambient temperatures up to 32°C. In fact, above 32°C CSP plants may experience efficiency losses due to the cooling system's underperformance. Moreover, significant efficiency losses (5% or more) become apparent above 37°C ambient temperature. Observed on a hot day, this underperformance reflects a reduction of the cycle efficiency due to high condensing temperatures which increases the turbine backpressure and the steam rejection temperature [7].

The second system is the indirect (or Heller) ACC system described in the Figure 14. In this system cooling water is sprayed directly into the exhaust flow of the steam turbine in a ratio of 50:1. The condensate water is either pumped to bundles of finned tubes arrayed in a cooling tower or pumped back to the boiler. Therefore, in the cooling tower the heated water is cooled by ambient air to be sprayed as cooling water in the condenser. For indirect ACC systems, either mechanical or natural draft can be used.

These two families can be combined into hybrid wet/dry cooling systems. Two categories of hybrid systems exist: those aimed at plume abatement and those aimed at reducing water consumption. The most interesting option for CSP plants is that aimed at reducing the water consumption compared to plants integrating WCC systems only. Moreover, during warm weather, the hybrid system allows one to enhance the performance compared to plants integrating only ACC systems. As described in Figure 15, hybrid systems are ACC/WCC parallel systems. The ACC system is the primary cooling system, but during warm weather the WCC reduces the load on the ACC and enhances the performance [7].

Environmental Aspects

The main environmental aspect that must be taking into consideration by a CSP designer, especially for those integrating a WCC system, is the water supply. As explained in the previous section, a WCC system uses a large amount of water, and the most sustainable solution is to implement the CSP beside a natural water source. If this option is possible, the second environmental impact will be the rejection of heated water into the original source which will increase its temperature in the CSP area (large river or sea). If a natural water source is not available, a reliable and sustainable water supply will have to be provided (pipelines, trucks or tanks).

CSP design should include the spatial occupation. In fact, by implementing large technologies such as cooling towers or water tanks the plants increase their occupancy area and visual impact [7].

Economic Aspects

As explained in the following table, water consumption of a circulating evaporative WCC system has been estimated for each CSP type from current plants depending on implemented technology. Those three main technologies use different heating systems, either heating steam directly or heating through a heat transfer fluid [7]. By utilizing an ACC system rather than a WCC system the CSP plant water usage can be reduced by as much as 90%. The utilization of a hybrid WCC/ACC system can provide water consumption reductions from 50% to 90% depending on the type of the technology used (CSP type as well as cooling system) [7].

CSP plant

Table 3: Comparison of efficiency reduction by cooling system for each type of CSP plant [7]

As explained in the previous section, implementation of ACC technologies, and especially direct ACC, can lead to plant efficiency reductions if the daily ambient temperature is not compatible with its requirement (ambient temperatures under 32°C). See Table 3. Evaporative WCC technology has the lowest capital cost amongst all cooling technologies for CSP plants. They are estimated to be about 2,800€/kW -4,800€/kW (excluding storage) [7]. See Table 4.

Table 4: Comparison of Capital cost increase by cooling system for each type of CSP plant [7]

WCC alternative cooling technologies offer much lower operation and maintenance costs linked to water management (water treatment, water freezing issues and the discharge of waste water). However, from an economic feasibility point of view, the operating and maintenance costs are minimal compared to the capital costs.

CSP plants integrating circulating evaporative WCC systems produce electrical power for about 7.6€c/kWh -9.2€c/kWh in south-western USA conditions [7]. See Table 5.

Table 5: Comparison of electricity unit cost increase by cooling system type of CSP plant [7]

Any alternative cooling systems to circulating evaporative WCC systems leads to a net electricity unit cost increase for CSP plants.

Technology being developed under WASCOP

The technologies being developed to reduce water consumption in CSP plants as part of the WASCOP project are now described briefly, together with their potential impact on water consumption. The technologies associated with cleaning are outlined in section 4.1 and the technologies associated with cooling are described in section 4.2.

Cleaning

From a cleaning perspective, WASCOP is developing technologies to reduce soiling on the collector mirrors and receiver tubes and to produce more water-efficient cleaning processes when cleaning is necessary.

Technology

WASCOP defines three specific objectives to improve cleaning process for CSP plants: The first way to reduce water consumption for cleaning is to reduce the need to clean the optical surfaces, namely the glass reflectors and the receiver envelope glass. This includes implementing dust barriers around the CSP plant's perimeter to prevent a significant proportion of dust and sand particles from reaching the internal solar field.

Anti-soiling coatings are being developed for the glass receiver tube. These coatings will be characterized and tested and subjected to accelerated ageing in order to assess their durability.

Previous studies by the WASCOP partners on anti-soiling coatings for glass reflecting mirrors have indicated a 2% specular reflectance gain to 30-50% loss soiling compared to non-coated reflectors. Thus, the WASCOP project is focusing on the final selection of existing innovative coatings. Similar to receiver glass, the durability of the coating for reflectors will be validated applying accelerated ageing tests on coated samples.

To improve cleaning management, WASCOP is developing three different soiling sensors [8]:

• The first soiling sensor will deal with the determination of the nature of the optical performance losses. This will be specific for evaluating the level, the type, and the gravity of • The second soiling sensor is designed to be low-cost to measure mirror soiling, measuring in real-time on the surface of the mirrors, or in close proximity [8].

• The third soiling sensor will provide a continuous measurement of the transmittance for the outer glass of the receiver tube [8].

The third and last way to reduce water consumption for cleaning is to optimize the cleaning operations. As an alternative to conventional wet cleaning, WASCOP will develop two different cleaning devices.

• A laboratory scale prototype of an ultrasonic cleaner will be developed. It will be a resonant sweeping wiper using agitation and cavitation, inside a very thin water layer on the optical surface to remove dust particles [8].

• Designed for central receiver plants, a gravity lip system at the rim of a heliostat will act as a wiper, using condensed water or rain to clean the optical surface [8].

Environmental Aspects

CSP plants are generally located in desert areas where high amounts of airborne particles are found as a result of the ground and local climate conditions. Each technology implemented for soiling mitigation should conserve soil cohesion within the plant area as well as in the proximity of the solar field. These can be complex issues. For example, dust barriers that enclose the solar field may prevent mirror soiling but their implementation generates new dust generation centres on the perimeter of the solar field unless regular removal occurs.

Moreover, ultrasonic cleaners could have an impact on flora and fauna (for example through insects), thus, WASCOP will provide management of this automatic cleaning system to reduce the impact of noise and vibration on the environment. In fact, one of the aims of WASCOP is to achieve a minimal impact on environmental flora and fauna by lower water consumption.

By reducing the cleaning frequency WASCOP will provide a small step towards reducing mirror abrasion from contact cleaning. By providing environmentally friendly solutions, not using additional cleaning fluids or surfactants, WASCOP will help CSP designers to control the environmental impact and wastewater [8].

Economic Aspects

All technologies previously presented will generate an extra cost in CAPEX. However, by providing a reduction of water consumption related to each technology (Table 6) and an improvement in reflectance through enhanced cleaning technologies, WASCOP will ensure absorption of the initial capital investment through the potential reduction of OPEX. In fact, the aim of the project is to provide solutions reducing the wet cleaning frequency and thus the operating intervention and water consumption.

Technology

Cooling

WASCOP aims to develop innovative solutions limiting water lost by evaporation. Therefore, by combining the cooling current practices described before and improving this combination with new technologies, WASCOP will provide economically and environmentally sustainable solutions saving water whilst maintaining the effectiveness of the CSP plant.

Technology

One of the major challenges confronting CSP plant operation is the consumption of large amounts of water, with studies showing that about 2000 to 3000 cubic meters per GWh water is used in overall CSP plant operation in areas where water is scarce and therefore costly. The cooling part of the power block producing electricity consumes about 80 -90% of the total water consumed in the case of wet cooling and it may go even higher when the ambient temperature increases.

Water savings for CSP plant cooling being developed under WASCOP require flexible and adaptive technologies for reducing the amount of water consumed in cooling, and are based on three strategies [8].

• Limitation of inconveniences

By limiting/reducing the amount of heat removed from the power block through convectional water cooling, it is possible to reduce the amount of water to be consumed in CSP cooling block [8].

• Optimization of the remedial strategies By developing and increasing flexibility for water management for cooling in the power block in relation with external environmental conditions through reviewing current CSP operating strategy it is possible to save the amount of water used in CSP cooling blocks [8].

H2020-LCE-02-2015

• Optimization of the remedial means By increasing the efficiency and sustainability of the cooling system and associated devices for better performance in relation to the reduction of water usage in the cooling block, reduced water consumption can be achieved [8]. See Figure 17. Three novel approaches being developed under WASCOP will reduce the amount of water consumption in the cooling block while maintaining the overall plant efficiency.

• Development of heat storage reservoir A heat storage reservoir will be added after the turbine condenser heat exchanger, on the power block cooling loop, to optimize the cooling process. This approach will reduce dry cooling loss of efficiency down to 5% while increasing the amount of water savings in cooling the power block [8].

• Development of hybridized cooler A cost effective dry or wet hybridized cooling system to use at peak ambient temperatures, will increase the overall plant efficiency by 5% and will also reduce the water consumed [8].

• Development of versatile adiabatic cooler A redesign of the heat exchanger and air handling systems with adjustable amounts of added water. Water savings in this approach, compared to conventional wet cooling is estimated at 90% [8].

HEAT STORAGE SYSTEM:

WASCOP will optimize the dry cooling strategy for heat rejection in the power block by taking advantage of low ambient temperatures during night time to develop a cost effective heat storage system which will be used to release all or part of the waste heat from the steam turbine to atmosphere when the ambient temperatures are lowest [8]. See Figure 18. WASCOP is developing a hybridized cooling system, taking advantage of seasonal temperature variations by employing dry cooling during lower ambient temperatures and wet cooling during the hottest periods of the year.

Steam flow exhaust from the turbine is channeled to the dry cooling system where the cooling process is achieved using high power fans during the coolest periods whilst during higher temperature periods of the year, exhaust steam from the turbine is cooled by the wet cooling system. See Figure 19 [8].

VERSITILE ADIABATIC COOLER:

The WASCOP project is developing a novel versatile adiabatic cooler based on the principle of an indirect dry cooler supplemented with an adjustable water supply to cope with the external environment in order to balance heat exchange performance, and maintain global plant efficiency.

Adiabatic cooling systems are well known. The Versatile Adiabatic Cooler being developed during WASCOP is based on the principal of an indirect dry cooler supplemented by an external water supply to cope with external climate conditions.

The versatile adiabatic cooler will be composed of finned tube heat exchangers topped with fans, and sprayers for water supplement options. The design of the versatile cooler will be optimized to control the amount of water in response to the external conditions. See Figure 20 [8].

Environmental Aspects

The impact of the versatile cooler system on the environment may include the following [8]:

• Change in ecosystem conditions due to increase in temperature (heat rejected by cooling system). • Noise in the CSP plant environment from the cooling system and fan.

• Reduced water discharges.

Economic Aspects

The cooling technology being developed under WASCOP will reduce water consumption up to 90% compared to conventional wet cooling technologies, which will save money on the assumption that the efficiency of the plant can be maintained. Table 7 below is based on the preliminary experiments performed by WASCOP project partners for validating the primary concept [8].

Advantages/Disadvantages of Innovative Cooling Systems

Advantage of Heat storage system:

• Heat storage system will reduce the expected decrease in plant efficiency during dry cooling by 2 -5%. • The system may be adapted to any kind of the turbine which may be cooled using ambient air.

• The system will reduce the cooler size for new plant and can be implemented in existing CSP plants.

• The system will significantly reduce the amount of water used in power block cooling since it makes dry cooling a more attractive option [8].

Disadvantage of Heat storage system:

• The heat storage system can be subjected to a thermal ratcheting phenomenon due to thermal stress accumulation during thermal cycles, which results in thermal stresses that may exceed the wall tank yield stress leading to plastic deformation [START_REF] Sassine | Rock-Bed Thermocline Storage: A Numerical Analysis of Granular Bed Behavior and Interaction with Storage Tank[END_REF].

Advantage of Hybridized cooling system:

• Hybrid cooling involves the use of different water volumes depending on the season. The system will save up to 80% of annual water consumption of the evaporative cooling tower. • The efficiency can be improved in wet, cold and hot environments by 3% in comparison to the current dry systems [START_REF] Colmenar-Santos | Water consumption in solar parabolic trough plants: Review and analysis of the southern Spain case[END_REF].

Disadvantage of Hybridized cooling system:

• The hybridized system requires additional components and systems which increase the cost of investment and maintenance. • The overall system cost will increase by 3-5% compared to wet evaporative systems such as cooling tower systems [START_REF] Colmenar-Santos | Water consumption in solar parabolic trough plants: Review and analysis of the southern Spain case[END_REF].

Advantage of versatile adiabatic cooler:

• The versatile adiabatic cooler will improve heat transfer performance, and eliminate the traditional trade-off between high heat transfer performance and low fouling sensitivity of the current adiabatic cooling systems. • Parasitic loads will be minimized through improving air handling and distribution on the versatile adiabatic cooler, therefore increasing the efficiency of the cooling system. • The innovative versatile cooler using the adjustable water technology will reduce water consumption in cooling of a CSP plant by up to 90% in comparison with wet cooling systems [8].

Disadvantage of versatile adiabatic cooler:

• The versatile cooling system will have high initial investment costs due to improved system design such as improved air handling and distribution of the cooler, fan drive unit, and improved heat transfer coefficient on the heat exchanger. • Power consumption of the system is high compared to wet cooling, as a result of an indirect mechanical draft cooling system [8].

Future/Other Technologies

Cleaning water recovery

So far, CSP plants use few or no cleaning water recovery devices and the water is directly disposed of on the ground. During cleaning operations, the soiled water could be collected and reused, providing that a system was developed to separate the contaminants from the clean water.

A membrane filtration system could be used to purify the water onsite. The filter may not require any power supply, with the liquid is driven by gravity [START_REF] Raza | Water recovery in a concentrated solar power plant[END_REF]. Then, the filtration operation could be done directly after water collection. Depending on the soiling composition and therefore the quality of the water collected, other type of water treatments could be required. Despite a few concerns about a water recovery system, a CSP supplier provides a solution based on a pneumatic enclosure, to facilitate the water collection [START_REF] Colmenar-Santos | Water consumption in solar parabolic trough plants: Review and analysis of the southern Spain case[END_REF].

In addition to recover the water from cleaning operation, this system can be used to collect water from rainfall or intense dew in the morning. Depending on the purity, the collected water could be used for others purposes, such as cooling [8].

Electrodynamic screen, self-cleaning CSP collectors

Electrodynamic Screening is a technology developed within Boston University [START_REF] Stark | Prototype Development for Self-Cleaning CSP Collectors[END_REF], which uses the electric charge of the dust particle to repel the dust. With a dust removal efficiency greater than 90% and a low electrical power consumption of the system, this solution has a strong potential and could be very efficient.

This cleaning method can be used as often as it is required to maintain an optimal mirror reflectance, and could be water free. It can be used in the event of a dust storm, to recover a sufficient reflectance, and allow the power plant to produce energy with minimal delay [8].

However, this technology will have shading effects and could prove costly to scale up from its current laboratory scale.

Cleaning with brushes and without water

Cleaning systems in CSP plants still use water for its reflectance recovery efficiency (up to 99% of original value). Inspired by PV solar farms technology, a water free method can achieve the same performance [13] [14]. A micro-fibre brush could remove dust from the surface of the panel and generate an air stream to blow off the dirt [START_REF]United States Patent Application Publication[END_REF]. The brush would need to be tested on mirror surfaces to study the adjustment to CSP plants. This technology may prove abrasive and be less efficient on reflecting mirrors than on PV panel covers.

In addition, the full cleaning machine exists only for a flat surface, such as that found in many heliostat fields, but is autonomous and operates on the panel frame itself. although it may need to replenish the water tanks during a cleaning shift. Clearly a driver is required to operate the vehicle and the electrical on-board systems that monitor the operation in order to have an efficient and safe cleaning for the mirrors. Despite taking considerable care, mirrors are occasionally broken during cleaning. Some manufacturers provide an autonomous driven machine [START_REF] Troughs | PARIS-Autonomus cleaning system for Parabolic Troughs PARIS-Autonomous cleaning system for Parabolic Troughs[END_REF] , but nothing exists to clean the mirror directly from the mirror frame, as can be seen for some PV panel power plants. Such guide railed machines are very accurate, can cover the whole mirror surface and then use the exact amount of water needed.

In addition, each row of mirrors could have its own cleaning device. Associated to real-time reflectance measurements, the cleaning operation could be performed automatically, only where it is needed. Moreover, after a dust storm event for example, the whole plant will be able to quickly clean itself. Then, it will lower the period that mirrors are inactive.

The challenge is to supply the water to the moving system but also to design a safe, efficient and profitable frame for the cleaning machine. Figure 23 shows what such a device could look like [START_REF] Fenati | Autonomous cleaning machine for parabolic mirrors[END_REF]. Most CSP plant operators prefer to use human labour to clean the mirrors. Truck drivers, the vehicle and the fuel needed can be seen as inexpensive. This method seems to be more profitable than autonomous devices, with their higher initial capital cost, and their unproven reliability record over an extended time period.

Advantages/Disadvantages of Novel Cleaning Systems

Technology

The process of blowdown occurs in the cooling towers by draining water with a high concentration of minerals to drain and replacing the lost volume of water with fresh water. This method prevents a substantial amount of minerals to build up in the water which would have a corrosive effect on the cooling tower materials and also create obstructions (blockages) in different components of the circuit.

The water used in this way indicates a significant opportunity for the case of water management. As this highly concentrated water is disposed of to drain the water is effectively wasted and in an area of water scarcity this is a very costly loss. However, by managing the concentration of minerals in this water more effectively (for example by adding a water treatment system) there is potential for huge savings. The saving can be made from reducing the annual usage of makeup water. Make up water is the fresh water added to the system and is the sum of the losses due to evaporation, drift and blowdown. The addition of scale inhibitors in the cooling tower water could also increase the saturation point of the water allowing a higher concentration of minerals in the water before scaling effects would occur.

Many cooling tower systems operate between two and four cycles of concentration (COC). However, by increasing this from (say) three to six, the reduction in make-up water can be up to 20% and the cooling tower blowdown by 50%. [START_REF]Best-management-practice-10-cooling-tower-management[END_REF] A feasibility study of a hybrid cooling system in a thermal power plant has been conducted by Williams et al [START_REF] Williams | Feasibility of a Hybrid Cooling System in a Thermal Power Plant[END_REF]. The aim of this was to investigate the reduction of water usage with a minimal impact of the plant performance. The study compares a hybrid system which is configured both in series and in parallel over a range of operating conditions. The results do show positive results as there is a significant reduction in water usage however due to the start-up cost of the system it has been found not to be economically viable as yet. If water and electricity costs were to significantly increase then this option would become more viable.

Environmental Aspects

The addition of chemicals to inhibit corrosion into the system may allow a reduction in water usage, however these chemicals can have an adverse effect on the environment. Therefore, post blowdown treatment must be done to remove or reduce these chemicals to allowable environmental limits.

Economic Aspects

The pre-and post-treatment system cost would incur additional initial expenditure. The overall financial gain however must be assessed by comparing the systems costs against the potential water savings.

Advantages/Disadvantages of Novel Cooling Systems

Possible technology Advantage Disadvantage Water Treatment

Reduction in yearly blowdown frequency resulting in a reduction of water usage.

CAPEX & OPEX costs.

Conclusion

To conclude, it has been found that a wet cooling system can maintain the plant efficiency, however it will consume up to 90% more water than a hybrid or dry system. The dry cooling system does not consume water as its primary cooling method is carried out with the use of electrically powered mechanical fans. This system will run off the electricity produced by the plant which in effect will reduce plant efficiency by up to 10%. The hybrid cooling system on the other hand will save up to 90% more water than the wet cooling option with only an efficiency loss of up to 3%. However, as stated in the previous section, the capital cost required can make this solution economically unattractive.

For the cleaning aspect of water management, the optimal solution has been found to be the alternate use of wet and dry methods. Initially, dry cleaning methods can be performed until a threshold level of reflectance has been met. Once the reflectance falls below this threshold a wet cleaning solution can be implemented to restore full reflectance to the mirrors. However, the combination of water and a rotating brush head has been found to be the most common method of cleaning within existing plants.

H2020-LCE-02-2015

GA number: 654479 WASCOP WP4, D4.2, V1. [START_REF] Turbo | Voith Turbo Develops Special Vehicles for Application in Thermal Solar Power Plants[END_REF]

Figure 1 :

 1 Figure 1: The traditional high-pressure, low water volume system

Figure 2 :

 2 Figure 2: High-pressure, low water volume, with rotating head rig system

Figure 3 :H2020

 3 Figure 3: The traditional deluge method in operation

Figure 4 :

 4 Figure 4: High pressure cleaner vehicle with cleaning brushes

Figure 5 :

 5 Figure 5: ECILIPM vehicle for solar mirror cleaning

Figure 6 :

 6 Figure 6: ALBATROS Cleaning VehicleFeatures of ALBATROS cleaning machine (Figure6):• The vehicle has two arms to enable cleaning of both upper and lower part of the collector simultaneously.• The vehicle has special software which guarantees the safety of the mirror and the receiver.• The vehicles requires only one operator and hence reduces operating cost.• The vehicles use brushes for cleaning to obtain maximum result at a speed between 1 -3 km/h. • The vehicles are built with special nozzles to clean the receiver tubes.

Figure 7 :

 7 Figure 7: ELCOS Vehicle for pressurised water-cleaning of solar mirrorsFeatures of the VOITH cleaning machine (Figure7):• Built originally for cleaning individual mirror facets.• ELCOS is fitted with special ultrasonic sensors that allow the sickle-shaped arm of the vehicle to work on the 6.40 meter-high mirror facets as closely as possible without touching them. • Creep speed of 6km/h. • ELCOS moves twice through each of the rows of mirrors and showers them down with water pressurized to 200 bar. • Equipped with relevant monitors and cameras to give the driver full overview of the process.

  Figure 8: PARIS Cleaning Vehicle

Figure 9 :

 9 Figure 9: FERMUPE Cleaning VehicleOverview of FERMUPE cleaning machine (Figure9): Fermupe has designed and developed two models of machines for cleaning solar mirrors and solar panels.

Figure 10 :

 10 Figure 10: ABENGOA Cleaning Vehicle

Figure 11 :

 11 Figure 11: Schematic diagram of the once-through WCC system [7]

Figure 12 :

 12 Figure 12: Schematic diagram of the circulating evaporative WCC system [7]

Figure 13 :

 13 Figure 13: Schematic diagram of direct ACC system [7]

Figure 14 :

 14 Figure 14: Schematic diagram of Heller ACC system [7]

Figure 15 :

 15 Figure 15: Schematic diagram of Hybrid system [7]

Figure 16 :

 16 Figure 16: WASCOP Cleaning approach scheme

  .2, V1.[START_REF] Turbo | Voith Turbo Develops Special Vehicles for Application in Thermal Solar Power Plants[END_REF] soiling. Using a combination of the Abengoa Condor and D&S 15R-USB technologies, this equipment will distinguish between the loss in reflectance due to soiling, abrasion or chemical degradation[8].

Figure 17 :

 17 Figure 17: WASCOP scheme for CSP cooling power block

Figure 18 :

 18 Figure 18: Scheme of heat storage on the cooling loop [WASCOP REPORT]

Figure 19 :

 19 Figure 19: Hybridized cooling system [WASCOP Report]

Figure 20 :

 20 Figure 20: Versatile Adiabatic Cooler

Figure

  Figure 21: EDS Principle

Figure 22 :

 22 Figure 22: Eccopia cleaning machine for PV panelsGuide rail and autonomous devicesCleaning machines for CSP plants mainly use a truck which transports the systems needed to clean the mirrors. The truck is able to carry the demineralized water needed for the cleaning operation,

Figure 23 :

 23 Figure 23: Cleaning railed robot for parabolic mirrors [16]

Table 1 -

 1 Figure 1 -The traditional high-pressure, low water volume system ................................................................ Figure 2 -High-pressure, low water volume, with rotating head rig system ................................................. Figure 3 -The traditional deluge method in operation .......................................................................................... Figure 4 -High pressure cleaner vehicle with cleaning brushes ........................................................................ Current Cleaning Systems Advantages/Disadvantages .................................................................... Figure 5 -ECILIPM vehicle for solar mirror cleaning ...........................................................................................Figure 6 -ALBATROS Cleaning Vehicle ...................................................................................................................... Figure 7 -ELCOS Vehicle for pressurised water-cleaning of solar mirrors ................................................. Figure 8 -PARIS Cleaning Vehicle ................................................................................................................................ Figure 9 -FERMUPE Cleaning Vehicle ........................................................................................................................ Figure 10 -ABENGOA Cleaning Vehicle .....................................................................................................................

Figure 11 -Schematic diagram of the once-through WCC system [7] ........................................................... Figure 12 -Schematic diagram of the circulating evaporative WCC system [7] ....................................... Figure 13 -Schematic diagram of direct ACC system [7] .................................................................................... Figure 14 -Schematic diagram of Heller ACC system [7] ................................................................................... Figure 15 -Schematic diagram of Hybrid system [7] ........................................................................................... Table

2

-Typical water requirements of an operating CSP plant depending on its cooling system .. Table

3

-Comparison of efficiency reduction by cooling system for each type of CSP plant

[7]

......... Table

4

-Comparison of Capital cost increase by cooling system for each type of CSP plant

[7]

....... Table

5

-Comparison of electricity unit cost increase by cooling system type of CSP plant

[7]

......... Figure 16 -WASCOP Cleaning approach scheme ................................................................................................... Table 6 -Water savings of WASCOP solution for cleaning [8] .......................................................................... Figure 17 -WASCOP scheme for CSP cooling power block ................................................................................ Figure 18 -Scheme of heat storage on the cooling loop [WASCOP REPORT] .............................................. Figure 19 -Hybridized cooling system [WASCOP Report] ................................................................................. Figure 20; Versatile Adiabatic Cooler ......................................................................................................................... Table 7: Innovative coolers ............................................................................................................................................. Figure 21 -EDS Principle ................................................................................................................................................. Figure 22 -Eccopia cleaning machine for PV panels ............................................................................................ Figure 23 -Cleaning railed robot for parabolic mirrors [16] ............................................................................ H2020-LCE-02-2015

H2020-LCE-02-2015 GA number: 654479 WASCOP WP4, D4.2, V1.3 Page 10 of 34 3.1.4. Advantages/Disadvantages of Current Systems

	Technology		Advantage		Disadvantage
	The traditional high-	Cleaning	attains	Uses relatively high
	pressure	rig/low-	reflectance value of the	volume of water
	water-volume		mirror at 0.72 litres of
			demineralized water per
			square meter of reflector
			surface
	High-pressure	with	Cleaning	attains	Uses relatively high
	rotating head rig/low	reflectance value of the	volume of water
	water volume		mirror at less than 0.72
			litres of demineralized
			water per square meter
			of reflector surface
	Traditional	deluge	Cleaning	attains	Uses relatively high
	method	(low-	reflectance value of the	volume of water
	pressure/high-water-	mirror at 0.87 litres of
	volume)		demineralized water per
			square meter of reflector
			surface
	High pressure cleaner	Cleaning	attains	Brush could damage the
	vehicle	with	reflectance value of the	mirror surface if not soft
	demineralized	water	mirror of about 99.7% of	enough
	and cleaning brush	its original reflectance.
			Uses	relatively	low
			volume of water

Table 1 : Current Cleaning Systems Advantages/Disadvantages

 1

Table 2 : Typical water requirements of an operating CSP plant depending on its cooling system H2020-LCE-02-2015 GA number: 654479 WASCOP

 2

		Parabolic trough	Solar tower	Fresnel technology
	Water requirements	2,955 -3,030	1,890 -2,840	3,785
	(in l/MWh)
	Steam temperature	400	1,050	535
	(in °F)
	Heat transfer fluid	Oil at 425°F	Water	Water

Table 6 : Water savings of WASCOP solution for cleaning [8]

 6

H2020-LCE-02-2015 GA number: 654479 WASCOP WP4, D4.2, V1.3

	Innovative	Water savings	Water usage on	Water usage on
	Cooling			current technology for	WASCOP technology
	Technology			50MWe CSP parabolic	for 50MWe CSP
				trough plant using wet	parabolic trough
				cooling	plant using wet
					cooling
	Hybridized cooler	50 -80% of	4000 cubic metre	800 -2000 cubic
		water	for	per day	metre per day
		cooling
	Versatile cooler	90% of water	4000 cubic metre	400 cubic metre per
		for cooling		per day	day

Table 7 :

 7 Innovative coolersSavings in water achieved by WASCOP technology will also lead to the reduction of OPEX cost which is strongly connected to the water consumptions of CSP plant, therefore providing a reduction in the number of CSP maintenance programs and increasing plant efficiency[8].

H2020-LCE-02-2015 GA number: 654479 WASCOP WP4, D4.2, V1.3 Page 27 of 34

H2020-LCE-02-2015 GA number: 654479 WASCOP WP4, D4.2, V1.3 Page 30 of 34 Possible novel cleaning technology

		Advantages	Disadvantages
	Water recovery	Save significant volumes of	-Disposal of the filtrated
		water without interfering with	particles
		the cleaning efficiency or	-Adaptation of the system on
		strategy	the mirror structure
			-Investment cost
	EDS	-Dust removal efficiency > 90%	-Shading effect
		-Autonomous	-Technology needs scaling up
		-Very fast (e.g. after a dust	-Investment cost
		storm): 2 minutes
		-Without requiring any water
		or manual labour
		-Strong potential
	Brushes without water -Does not use any water	-Must be adapted from PV
		-Dust removal efficiency, up to	panel usage
		99%	-Need to study the behaviour
		-Commercialised technology in	on mirror surface (durability
		PV solar farms	question to be answered)
	Guide railed devices	-Accurate	-Supply the demineralised
		-Available on each mirror row	water to the onsite system
		-No manual labour required	-Unproven in practice
			-Investment cost
	5.2. Cooling

H2020-LCE-02-2015 GA number: 654479 WASCOP WP4, D4.2, V1.3 Page 32 of 34