Tatiana De-Wyse

Emmanuel Renaux

José Mennesson

Using sketch recognition for capturing developer's mental models

Keywords: Sketch, UML, Image recognition, Engineering Process, empirical study

The purpose of agile practices is to optimize engineering processes. Beside it, software documentation often suffers from the priority given to fast and successive deliveries of new functionalities. As a consequence, incomplete documentation and graphical representation make it difficult for a developer to maintain and evolve. Sketching is an integral part of the software design and development. Indeed, among other stakeholders, developers use sketches to informally share knowledge about source code. Since sketches are often hand-drawn, written on paper or whiteboard without any additional technology tool, they are not considered as an artifact in agile method or traditional engineering process but as a disposable production.

In this work, we focus on sketches containing Unified Modeling Language (UML) diagrams. To produce documentation or to exploit information from this kind of sketches, developers have to transcribe it in a UML case tool, what they see as a waste of time that hinders productivity. We argue that sketches might be worth considering as non-code artifacts. The fact is that developer or designer drop informally a lot of information about the software, which is unusable.

Our goal is to verify that simply capturing sketches can provide useful information for later use and then improve the modeling process efficiency. In this paper, we present a preliminary approach that consists in automatically capture information from the sketches using image processing and pattern recognition. We propose a fledgling prototype that demonstrates the proposal's viability. Then, as a future work, we plan to put in the hands of the developers a finalized version of our prototype and study the added value of our proposal.

I. INTRODUCTION

More and more developers are adopting agile practices, no matter if it is a fad or a pragmatic evolution of methodologies. In agile approaches, the source code is considered as the most valuable artifact. It leaves aside other means to describe the architecture or functionalities like models and other human-readable documents. Nevertheless, developers create and maintain non-code artifacts [START_REF] Ghazi | Challenges of working with artifacts in requirements engineering and software engineering[END_REF] when developing and evolving software. These non-code artifacts help to express the requirements, understand the software architecture or document the source code. Each one is dedicated to specific stakeholders and allows to communicate, exchange and maintain the system. The Unified Process [START_REF] Kruchten | The Rational Unified Process: An Introduction, Second Edition[END_REF] precisely defined different types of artifacts throughout the project lifecycle, in order to ensure high-quality software production. The agile methodScrum [START_REF] Schwaber | Scrum development process[END_REF], implies more roughly documents like user stories, programs, tests cases, and other deliverables created before designing and implementing the system.

Among these artifacts, graphical representations are widely used to model the system, as in Industrial Software Development [START_REF] Störrle | How are conceptual models used in industrial software development?: A descriptive survey[END_REF]. UML 1 [START_REF] Rumbaugh | Unified Modeling Language Reference Manual, The (2Nd Edition)[END_REF] is a standard modeling language and a graphical representation that fosters communication and visualization of software architecture. As a graphical representation of the system, it helps to understand, discuss or brainstorm about it. The strength of UML is that any developer understands it since he learned it at school and it is the de facto standard for modeling and designing information system.

However, UML is often subject to negative press and is considered as a waste of time. But surveys like [START_REF] Michel | How effective is UML modeling ? -an empirical perspective on costs and benefits[END_REF], [START_REF] Petre | UML in practice[END_REF] highlight that although many engineers say they do not use UML, they use it informally. In fact, engineers actually admit doing sketches of UML diagrams [START_REF] Baltes | Sketches and Diagrams in Practice[END_REF], [START_REF] Mangano | How software designers interact with sketches at the whiteboard[END_REF] during activities like:

• Sharing knowledge about the code in collocated design collaborations [START_REF] Dekel | Notation and representation in collaborative object-oriented design: an observational study[END_REF] • Exchanging ideas in design software session with easy transitions between different types of sketches like lists, tables, GUIs, ER diagrams, class diagrams, code, drawings. Sketches support mental simulations, reviews of designers progress and discussions of alternative [START_REF] Mangano | How software designers interact with sketches at the whiteboard[END_REF] • Creating informal graphical representations to quickly document workflows. Sketches help the comprehension of code [START_REF] Baltes | Sketches and Diagrams in Practice[END_REF] • Supporting their development workflows by helping them for different purpose and context [START_REF] Walny | Follow that sketch: Lifecycles of diagrams and sketches in software development[END_REF] • Finally, externalizing their mental models of source code.

Tools do not allow to support face-to-face communication [START_REF] Cherubini | Let's go to the whiteboard: how and why software developers use drawings[END_REF] In the next section, we highlight some obstacles to the adoption of UML in traditional engineering practices. Since there is little documentation of the software architecture, in section III, we justify the use of sketches to capture developers' mental models of their source code. Related work focuses on sketching tools in section IV. In section V, we introduce our prototype that captures and understand sketches without any effort from the developers. In the prototype, we firstly restricted our focus to the UML class diagram. Finally, we discuss a survey we need to further validate our work and the next steps that have to be created to prove that capitalizing on sketches can improve the development process efficiency.

II. WEAK ADOPTION OF UML IN IT PROJECTS

Documentation and modeling tasks require significant time to be produced and maintained. IT project engineers do not maintain quality models since they see them as a waste of time and effort. Although this proposal is controversial [START_REF] Petre | UML in practice[END_REF], [START_REF] Störrle | How are conceptual models used in industrial software development?: A descriptive survey[END_REF], developers prefer to use this time and effort in implementing new functionalities, passing tests and solving problems. Moreover, agile practices do not help the adoption of UML as they speed up the pace of deliveries. To counter these adverse developments, we selected in the literature, three obstacles to the adoption of UML or other graphical representations in IT projects. Sketching is the more natural way to draw UML models, even if they are not as formal as in a UML tool. We think and want to verify that more sketches in projects and a simply manner to exploit them can help to counter these obstacles, and reintroduce software architecture modeling as an efficient practice.

A. Operational software rather than comprehensive documentation

Agile practices encourage short development cycles to produce a shippable product increment. In these methods highly used nowadays, documentation and models are seen as a burden artifact [START_REF] Stettina | Necessary and neglected?: An empirical study of internal documentation in agile software development teams[END_REF]. Hence, the code and testing have taken the leadership over exhaustive documentation and graphical design of the software architecture. In terms of tools, teams mainly use development tools and project management tools like Jira 2 . In a simplistic view, the only non-code artifacts are the user stories. They make it possible to organize sprints and collaboratively monitor the project on a Kanban board. Globally, agile processes leave little room for modeling tools and models. Quick brainstorming sketches contain information about the source code architecture. It would be nice to be able to easily retrieve some of this information.

B. Inadequacy of Point&Click tools

Even if project management tools are used, in [START_REF] Gayane Azizyan | Survey of agile tool usage and needs[END_REF] Azizyan et al. show that collocated teams prefer physical walls and paper. Developers also not use Point and Click solutions to model or maintain software architecture and its behavior but prefer to use paper or whiteboard. They find them more flexible [START_REF] Sedano | Software development waste[END_REF] as paper and whiteboard allow developers to use their own graphical representation (informal notation). They used the sketch as memory help for debugging, explaining task and refine or improve their idea [START_REF] Walny | Follow that sketch: Lifecycles of diagrams and sketches in software development[END_REF]. Then, if it is required it allows the preparation of formal documentation and developers transcribe sketches in a formal way [START_REF] Baltes | Sketches and Diagrams in Practice[END_REF]. Sometimes, to deal with the lack of documentation of the source code, software visualization tools and reverse engineering tools automatically provide the UML representation of code for better understanding and localization of code source. For example, this helps developers to take in hand unfamiliar code [START_REF] Lee | What situational information would help developers when using a graphical code recommender[END_REF]. Finally, 2 https://fr.atlassian.com/software/jira Point & Click diagramming tool suffers from the lack of collaborative capacities. In collaborative activities, co-workers need awareness about artifacts and other coworkers. They need information about who, how and why things have been done and then they have to exchange with other co-workers [START_REF] Ko | Information needs in collocated software development teams[END_REF]. The use of these point & click solutions could be reduced by trying to retrieve both the information contained in the sketches and other contextual information.

C. Cognitive shift due to the proliferation of tools

The need for knowledge exchange and code explanation fosters the use of communication and social tools [START_REF] Margaret | How social and communication channels shape and challenge a participatory culture in software development[END_REF]. This increases even more, the number of tools used by developers. Switching from one tool to another or interruption due to instant messaging, for instance, cause cognitive shift and knowledge loss. This reduces the efficiency of the development process and increases the feeling of a waste of time. Surveys like [START_REF] Ghazi | Challenges of working with artifacts in requirements engineering and software engineering[END_REF] show that existing tools do not provide sufficient support for carrying out some tasks like visualizing artifacts that are larger than their screen and with interconnected artifacts that have to be accessed simultaneously. The methods to deal with that problem are inefficient and frequently error-prone. By reducing the number of tools and exploiting the engineer's workstation equipped with a keyboard and draft paper containing his sketches, we wish to limit the cognitive shift.

Let's see in the following how we plan to counter these obstacles through a better consideration of sketches.

III. CAPTURING MENTAL MODEL THROUGH SKETCHES

RECOGNITION

In traditional development processes, lack of documentation is a factor of loss in efficiency. For the reasons we gave previously, developers do not produce an exhaustive UML model of their source code. However, they create and maintain mental models of the architecture of the code. Inevitably there is a loss of code knowledge and developers spend time understanding and trying to recover the rationale behind the code. They rely on and interrupt their co-workers to find answer when they get stuck by a task [START_REF] Latoza | Maintaining mental models: a study of developer work habits[END_REF]. The consequence is that the code becomes hard to debug and complex to maintain [START_REF] Fernández-Sáez | On the use of UML documentation in software maintenance: Results from a survey in industry[END_REF].

Understanding the rationale behind source code is the biggest problem for developers. Graphical representations help to understand, to discuss, to brainstorm, etc. Sketches are an example of informal visualizations that are often created when understanding or explaining the source code [START_REF] Baltes | Sketches and Diagrams in Practice[END_REF]. Moreover, hand-drawn is a natural and flexible way to model a domain. And even if the sketch do not respect formal notation of modeling language they foster later their creation. Contrary to the point & click solution that requires the respect of their notation, hand-drawn do not constraint developers. Surveys have revealed that developers make sketches, which often contain UML diagrams [START_REF] Baltes | Sketches and Diagrams in Practice[END_REF], [START_REF] Störrle | How are conceptual models used in industrial software development?: A descriptive survey[END_REF].

We base our work on the fact that sketches and diagrams give an accurate picture of parts of the developers' mental model and help to understand a software project [START_REF] Latoza | Maintaining mental models: a study of developer work habits[END_REF]. But since the sketch life cycle is short [START_REF] Walny | Follow that sketch: Lifecycles of diagrams and sketches in software development[END_REF], our goal is to design a tool that could help us to analyze transparently the sketches and to exploit the information contained. We want to improve the software development process using graphical representation more efficiently with a minimum of effort and without disturbing the development activity. The key success factor is for us: capturing the mental model of the developers in their sketches through sketch recognition.

IV. RELATED WORK

There are a lot of sketching tools since the different devices allowing sketching activity are numerous. In software design and development practices, only a few of them allow the recognition of sketch elements and their semantic meaning as shown in table I. Here is a list of available sketching tools we found in a comprehensive survey of the literature.

A. Tools allowing digital sketching activity

Digital Sketching defines a sketching activity realized thanks to an electronic device like digital pen or finger on a touch screen, or mouse for computer desktop. This category also contains e-whiteboard 3 with the hand gesture or digital pen.

a) Formal early design: This category of tools allows expert designers equipped with an electronic device to freely create sketches that will be transformed automatically in formal models. SUMLOW [START_REF] Chen | SUMLOW: early design-stage sketching of UML diagrams on an e-whiteboard[END_REF] is a Visual Basic application for early design stage sketching of UML diagrams on e-whiteboard. It uses Rubine Algorithm for text recognition and multi-gesture algorithm for shape recognition. It allows annotation of the digital sketch drawn and their format transformation to be used later in a case tool. MaramaSketch [START_REF] Grundy | Supporting generic sketching-based input of diagrams in a domain-specific visual language meta-tool[END_REF] is an extension of a Marama meta-tool, a set of core Eclipse4 plug-ins that provides diagram and model management. It allows sketch drawn with tablet PC stylus, mouse, e-whiteboard pen. Recognition of shape and text element on a sketch is made by HHREco toolkit 5 . Tahuti [START_REF] Hammond | Tahuti: A geometrical sketch recognition system for uml class diagrams[END_REF] allows a user to draw UML class diagram with a computer mouse. Recognition of geometrical element of a class diagram is performed by analyzed stroke made by the user when he is drawing on Tahuti. In [START_REF] Lank | On-line recognition of uml diagrams[END_REF], Lank et al. present a prototype that allows to drawn sketches thanks to e-whiteboard, tablet, and mouse. Recognition of what the user has drawn is made by analyzing pen stroke. Segmentation and correction of what is drawing are realized by the user to let him drawn freely without any computation time feeling.

The drawbacks of these tools are: first, developers need to be equipped with an electronic device, and second, the original sketches are lost, and also a lot of information expressed informally.

b) Informal early design: Informal sketching tool fosters the communication and collaboration between different stakeholders. Calico [START_REF] Mangano | Supporting informal design with interactive whiteboards[END_REF] is a system for e-whiteboard which proposes a canvas for managing and drawing a digital sketch. No recognition of what is drawn is provided and links between sketches have to be done manually by the user. Gambit [START_REF] Braga Sangiorgi | User interface design by collaborative sketching[END_REF] supports collaborative early design session of user interface (UI). Designed according to a client-server model, it can store and retrieve digital hand-drawn sketches. Users of Gambit can annotate and use it on a tablet, desktop, and videoprojector. AugIr [START_REF] Kleffmann | Evaluation of a traceability approach for informal freehand sketches[END_REF] provides an interaction room system by combining multiple e-whiteboards. Stakeholders can manipulate hierarchical canvas and freely organize their views. Size, direction and temporal context of drawn stroke are analyzed to recognize meaningful text and match the artifact with link automatically. IdeaVis [START_REF] Geyer | Ideavis: a hybrid workspace and interactive visualization for paper-based collaborative sketching sessions[END_REF] is a tool for co-located sketching sessions. It uses digital paper and pen for sketch creation and manipulation on large interactive display for presentation. FlexiSketch [START_REF] Wüest | Flexisketch: A mobile sketching tool for software modeling[END_REF] and his extension FlexiSketch Team [START_REF] Wüest | FLEXISKETCH TEAM: collaborative sketching and notation creation on the fly[END_REF], allows collaborative sketching. Symbols recognized are manually pre-defined by user and store in a library for later re-use and recognition if it is redrawn by users.

On the one hand, informal sketching fosters the communication and collaboration of stakeholders by providing a totally informal way of explaining ideas and brainstorm. On the other hand, this type of tool does not take into account the hidden figures, the developers, the code and the analog sketches done beneath the design session.

c) Code explanation/modeling: Tools built on code explanation and modeling increase the comprehension of the rationale behind the code written by developers. CodeGraffiti [START_REF] Lichtschlag | Codegraffiti: Using hand-drawn sketches connected to code bases in navigation tasks[END_REF] is a prototype for Adobe Bracket 6 which links informal digital sketch on a tablet and smartphone to source code artifact. OctoUML [START_REF] Vesin | Octouml: An environment for exploratory and collaborative software design[END_REF] supports collaborative UML modeling during any phase of the software design process, in allowing formal and informal notations on a touchscreen, e-whiteboard, and computer with mouse interaction. It allows connection with version control and source code management.

Even if these tools step the code explanation and documentation up, analog sketches are totally dismissed. Furthermore, developers that doing sketches, need to take in hand a new tool and redraw every diagram created on analog support.

B. Tools allowing Analog Sketching

Analog sketching tools allow taking into account the whole sketch.

a) Informal code documentation: SketchLink [START_REF] Baltes | Linking sketches and diagrams to source code artifacts[END_REF] is based on a server, a web application and an IDE (Integrated Development Environment) plugin 7 to allow the sketching LivelySketch [START_REF] Baltes | Round-trip sketches: Supporting the lifecycle of software development sketches from analog to digital and back[END_REF] enables the capture and the annotation of analog sketch and diagram by provided a REST API 8 . Identification of a sketch is done by a printed QR code with predefined UUID (Universally Unique Identifier). Links between sketches need to be done manually by touch gesture and/or mouse. Although the sketches are captured and stored, there is another step required to fully take into account the meaning of sketches drawn.

All these tools are the example of the wide variety of devices and technologies that can foster the documentation in any step of the software development process. However, if the workplace is not fit with them, sketching with these tools is useless [START_REF] Socha | Sketching and conceptions of software design[END_REF].

The majority of the tools in this related work use online recognition to retrieve information present in a sketch. Indeed, online recognition is done by real-time analysis of the stroke made by the user when he draws on an electronic device. Offline recognition is another challenge. Unlike the online approach, offline recognition needs to capture and digitize the sketch before being analyzed. This analysis consists of a combination of many image processing algorithms. This complex mining activity results in finding elements contained by the image. Some methods dealing with digitized documents recognition in other domain like architectural plan [START_REF] Ghorbel | Interactive interpretation of structured documents: Application to the recognition of handwritten architectural plans[END_REF], electronic diagram [START_REF] Rabbani | Hand drawn optical circuit recognition[END_REF], flowcharts [START_REF] Herrera-Camara | Flow2code: From hand-drawn flowcharts to code execution[END_REF], and geometric shape [START_REF] Song | Retrieving geometric information from images: the case of hand-drawn diagrams[END_REF] can be found. This reveals the possibility of recognizing UML elements on a bitmap image.

V. OUR APPROACH

In [START_REF] Michel | A vision on a new generation of software design environments[END_REF] Chaudron and Jolak revealed their vision of a new generation of software design environments. We try to approach this vision by raising analog sketches to the level of project artifacts. We choose offline mode 1) to let the designer uses any kind of support and 2) to not impose him yet another tool to manage. To this end, analog sketches need to 8 Application programming interface Representational State Transfer be digitized with for instance a smartphone and recognized to understand part of their semantic meaning. To partly respond to the need for collaborative capacities like awareness about artifacts and other coworkers, some metadata can be added to the sketch automatically or manually to better identify its context.

Our goal is to use sketches to capture efficiently the mental model of the developer without asking him to transcribe it. Analyzing digitized hand-drawn sketches is a complex task [START_REF] Bhowmik | Text and non-text separation in offline document images: a survey[END_REF]. Contrary to digital sketching, sketches on paper and whiteboard are considered as offline documents. In this sense, we do not have any information on the real-time tracking of the pen when shapes or text are drawn. So, it is difficult to segment the image and to recognize shapes and text which are not normalized. Finally, multiple challenges exist with a hand-drawn document:

• handwriting style differs from one person to the next,

• non-uniformity in size, shape, and orientation of handwritten elements, • text and shape separation due to touching elements, • text typography does not respect any known font. In this section, we propose an empirical approach based on classical tools in image processing to conduct a proof of concept. The first step is the pre-processing. The image needs to be cleaned to have the correct format to optimize image processing algorithm. In our study, a simple example of a class diagram (Fig. 2) is used to understand what kind of processing we need to employ. Indeed, components of hand-drawn class diagram depend on the way the user has drawn it. For example, boxes representing classes and lines representing links between classes can vary a lot from a user to another. In this context, extracting straight lines using classical methods as Hough transform [START_REF] Duda | Use of the hough transformation to detect lines and curves in pictures[END_REF] are no longer efficient as lines are more or less straight.

A. Pre-Processing phase

B. Shapes and text separation

In the case of hand-drawn elements, like in Fig. 2, contours of class boxes are not closed. Morphological closing [START_REF] Serra | Image Analysis and Mathematical Morphology[END_REF] is used to obtain connected components (Fig. 3(b)). Suzuki algorithm [START_REF] Suzuki | Topological structural analysis of digitized binary images by border following[END_REF] is then used to retrieve contours from the image (Fig. 3 (c)). As there is a line between classes, only one connected component is obtained for the two classes and the association between them (Fig. 4(a)). In order to separate them, connected components are filled (Fig. 4(b)) and a morphological opening is performed. By using the previous image as a binary mask on image obtain in 3(b), only classes are obtained (Fig. 4(c)). 5c) and class box component containing attributes name (Fig. 5b). Classical recognition methods can be then used to recognize shapes and classical Optical character recognition (OCR) as Tesseract11 can be used to recognize the handwritten class name (Fig. 5).

VI. FUTURE WORKS

A. Finalization of the sketches recognition tool

The purpose of the work presented in this section was to elaborate the specifications of our sketch recognition mechanism. For the moment, we have only made a very simple prototype to validate that it is possible to automatically extract information from the sketch. The few tests carried out have been promising. So we plan the future steps to continue developing our tool as shown in Fig 6 . First, we will need to complete the segmentation of shapes and text in more complex diagrams. Then Tesseract engine will be used to perform OCR recognition of names, methods, attributes, cardinalities. For shape features recognition, we will need to build a corpus of hand-drawn class diagrams. Ho-Quang et al. [START_REF] Ho-Quang | Automatic classification of uml class diagrams from images[END_REF] have built a similar base of digitized images of class diagrams built with UML tools and found on the internet. Our base of hand-drawn CD diagrams will be used to train a classification algorithm analyzing shape elements to extract the information concerning size, orientation and type. Then, this trained classifier will be able to identify elements of a CD diagram. Once completing these steps and combining their results, we hope to obtain sketches with augmented information.

B. Experimentation

The goal of our work is to verify that more sketches in projects and our recognition and classification mechanism can help to reintroduce software architecture modeling as an effective practice in the engineering project.

We plan to do an experimentation and evaluation of our prototype with IT professionals and students. The first step is to evaluate the interest of an assisted archiving of the sketches. The second one is to evaluate the added value of later retrieval in a new project. And the third one is to evaluate the added value of the archived sketch to understand a code that the engineer did not write, for instance, a new team member.

a) Assisted archiving: We are designing our tool with ease of use in mind. We will ask the practicing software engineer to photograph his sketch with its smartphone. Our tool will archive the sketch recognizing the maximum of content semantic (shape, text, etc.) and adding context data from other tools like the code editor, the project calendar, etc. The first part of the evaluation is to observe the developer using our prototype, to find out if it is easy to use and if it helps to increase the use of sketches in IT projects, and if it encourages improvement of the quality of the sketches. And finally, we want to know if the engineer accepts to use and disseminate his sketches as documentation of its code.

b) The case of a new project: In the case of a practicing engineer brainstorming on a new project, we experiment with two types of engineer, the first one starts to draw sketches as he used to do, the other receives help from our tool. In the second case, he takes a photo of the draft of a sketch and the tool shows him all the corresponding sketches. Will it be a help for an engineer? And what use does the engineer have of it?

c) The case of a new team member: In the same way, we will compare two types of engineer. The first one tries to discover the code without help. The other selects a piece of code and our tool shows him the corresponding sketches.

VII. CONCLUSION

In this paper, we list some known reasons why documentation and graphical representation are important for the software to be designed and maintained. We focused on UML models. Then we select in the literature some obstacles to the adoption of UML diagrams to accompany the software development process. Our motivation is to verify the idea that using hand-drawn sketches that every developer naturally does, can improve traditional development processes or agile approaches. To set up experiments we elaborate a mechanism simply to use without adding any cognitive load to the developers. This mechanism uses off-line sketches recognition techniques. We describe our simple prototype that confirms we can automatically extract information from the sketch. So we plan the future steps to continue developing our tool. Finally, we present as a future work different cases we want to experiment to check the viability of our proposition in actual IT projects and to verify if our proposition really improves the quality and effectiveness of software modeling process.

Fig. 1 :

 1 Fig. 1: Prototype advancement

Fig. 2 :

 2 Fig. 2: Picture of a simple class diagram

Fig. 3 :

 3 Fig. 3: (a) binary image, (b) connected components extracted, (c) contours extracted

Fig. 5 :

 5 Fig. 5: Segmentation result of class diagram

Fig. 6 :

 6 Fig. 6: Overview of the entire future prototype

TABLE I :

 I Table of tools supporting sketching activity activity on source code artifact. Analog and Digital sketch can be stored and have to be manually linked on Java code source project with the rectangular marker. It fosters HTML documentation creation on the source code.

electronic whiteboard

http://www.eclipse.org/

https://ptolemy.berkeley.edu/projects/embedded/research/hhreco/

http://brackets.io/

https://www.jetbrains.com/idea/

https://github.com/tesseract-ocr/tesseract


Esteban-Decloux Martine
email: martine.esteban-decloux@agroparistech.fr

Deterre Sophie

Kadir Samir

Giampaoli Pierre

Albet Joël

Joulia Xavier

Baudouin Olivier

Affiliations

Two industrial examples of coupling experiments and simulations for increasing quality and yield of distilled beverages

Keywords: continuous distillation, batch distillation, simulation, neutral spirit, bitter orange, ProSim

The aim of this study was to check the ability of ProSim® software to model both continuous and batch distillations of two specific industrial units in order to obtain a both a better understanding of the behaviour of aroma volatile components and a tool to optimise the still's operation. Simulations of multistage continuous distillation to produce neutral spirit from raw alcohol and of batch distillation to produce bitter orange distillate from bitter orange peels macerate were carried out with ProSimPlus and BatchColumn software. Simulations were compared with distillations performed in two industrial plants. For each case, the industrial plants were studied to determine all the operating parameters and the behaviour of certain compounds selected for their high concentration or quality impact. Then, the NRTL and Henry's law A c c e p t e d M a n u s c r i p t 2/2 thermodynamic models were chosen. Simulation results of particular compositions of the selected compounds in the different extractions were analysed and compared with experimental measurements. Simulations represented faithfully the behaviour of compounds in the industrial plants. Therefore, it was possible for two totally separated cases to illustrate the interest of simulation software; for neutral spirit production to determine new operation set points in order to maximise productivity and improve quality for neutral spirit production and for bitter orange distillate production, to explain the choice of different cuts and the role of the presence of peels during distillation.

Introduction

Process simulators are very powerful tools that are scarcely used in food processing [START_REF] Bon | Simulation and optimization of milk pasteurization processes using general process simulator (ProSimPlus)[END_REF]. The two main obstacles for their use in food processing are the lack of data on the different compounds involved and the complexity of the processes (Joulia, 2008).

Among the process simulators used to represent continuous distillation, ProSimPlus is a process engineering software package that performs rigorous mass and energy balance calculations for a wide range of industrial steady-state processes (ProSim, 2012). It was used by Decloux and Coustel (2005) to simulate a typical production plant of neutral spirit, which is a high purity ethanol used in the food, pharmaceutical and chemical industries. The entire distillation plant was comprised of a series of seven columns for concentrating and purifying, and six representative compounds plus ethanol and water were considered. Vapour-liquid equilibrium data were assessed using the UNIquac Functional-group Activity Coefficient (UNIFAC) modified Dortmund model (Gmehling et al., 1993), a group contribution predictive model available in Simulis Thermodynamics which is the thermodynamic properties and phase equilibria server for ProSim software. Their results allowed them to illustrate the specific role of each column in the sequence of purification steps performed during neutral spirit production. Nevertheless, they did not validate their results on an industrial scale. More recently, [START_REF] Batista | Computer simulation applied to studying continuous spirit distillation and product quality control[END_REF] simulated continuous Cachaça production with the Aspen Plus simulation software. They took into account ten compounds plus ethanol and water. The entire representation of the liquid-vapour equilibria by the Non Random Two Liquids (NRTL) model required the knowledge of three binary interaction parameters for each of the sixty six associated binary mixtures. Binary parameters were adjusted for forty-three binary mixtures from literature data. For the others, they used the NRTL parameters available in the Aspen databank or estimations from the UNIFAC predicted values. Simulation results were compared with data from the Santa Adélia Mill plant, which produces 300 m 3 of anhydrous ethanol on a daily basis. Good agreement between the simulated and experimental results made it possible to study the role of several process parameters and the degassing system. The same research group [START_REF] Batista | Computational simulation applied to the investigation of industrial plants for bioethanol distillation[END_REF] validated the simulation of a typical bioethanol distillation process by considering an alcoholic wine with nineteen components. More recently, [START_REF] Valderrama | Advances on modeling and simulation of alcoholic distillation. Part 2: Process simulation[END_REF] ChemCad process simulating software to simulate the behaviour of eight congeners in the production of neutral spirit from beer. They studied more particularly the variation of congeners' behaviour due to variation of the alcohol grade of the feed from 8.3 to 14.8% v/v using the NRTL thermodynamic model. Therefore, they were able to suggest a tool to optimise the positions and flows of lateral extractions.

Concerning the extraction of orange essential oil extraction, the recovery of aroma compounds from orange essential oil was simulated by Haypek et al. (2000) using the commercial steady state software Pro/II and the UNIFAC model for the calculation of vapour-liquid and liquid-liquid equilibria of fourteen compounds plus ethanol and water. Since they obtained good agreement between the Cutrale industrial processing plant and simulated values for the first column used in the recovery of the aroma compounds from the aqueous stream during orange juice evaporation, they used the simulation to study the recovery of the aqueous essence phase.

Simulation of batch distillation is much more difficult than steady state simulation. [START_REF] Osorio | Rigourous dynamic modeling and simulation of wine distillations[END_REF] developed a mathematical model for simulating Pisco distillation as a multi-component reactive batch distillation process with reflux. In another study, [START_REF] Osorio | Wine distillates : practical recipe formulation for stills[END_REF] investigated, via process simulation, the operating recipes to obtain a distillate with a maximum of linalool and a minimum of octanoic acid. This recipe was validated with lab-scale experiments. [START_REF] Scanavini | Cachaça production in a lab-scale alembic: modeling and computational simulation[END_REF] studied Cachaça production in a lab-scale alembic. In that specific case, they modelled the alembic and compared the simulated and experimental results with seven components plus ethanol and water.

As the development of a simulation code is a time consuming task, both for setting up the problem and for solving it, [START_REF] Zamar | Operation planning in the rectification of essential oils[END_REF] In this study, the UNIFAC predictive model was replaced by the NRTL with binary interaction parameters fitted on experimental data from literature. Once the plant was modelled, it first was possible to suggest several modifications of the circuits between the columns and then to use the optimisation capability of the software to search new control set points of the many fluxes between the columns with the view of maximising the productivity by reducing the ratio of low grade alcohol produced. The second part presents a batch distillation simulation with BatchColumn software in order to obtain bitter orange distillate. The aim was to develop a tool to explain the rationale behind the process control parameters, in particular, the choice of the separation of distillate fractions.

Simulation of the rectification plant of neutral spirit process

Description of the neutral spirit continuous distillation plant

Neutral spirit is produced by distillation from a must obtained by fermentation of diluted beet molasses or beet juice with Saccharomyces cerevisiae yeasts. This solution is commonly called wine, regardless of the raw material. The wine, composed mainly of water and ethanol (ethanol mass fraction around 0.08), contains volatile components at a lower concentration than ethanol that are referred to as congeners (esters, alcohols, aldehydes, acids or bases, amines or sulphur compounds). Production of neutral spirit from wine is achieved in two stages: 1) production of a raw alcohol with an ethanol concentration in volume around 93% v/v containing concentrations of volatile congeners that are greater than desired, followed by 2) purification of the raw alcohol by rectification to eliminate the remaining volatile congeners. This last separation unit requires at least four interconnected and energy integrated distillation columns (Decloux and Coustel, 2005). This study focused on the purification stage of raw alcohol. Figure 1 presents the rectification unit of the industrial plant. It was composed of four columns named C30, C40, C50 and C60 and a liquid-liquid separator (decanter) S60.

Figure 1

A c c e p t e d M a n u s c r i p t

6/6

Each column had a specific role. C30 (fifty-two trays) was an extractive distillation column with water as the solvent. In this column, most of the congeners were removed into the head (H30). Water came partly from C40 bottom residue (B40) and partly from demineralised water (DW30). For good separation, Jacques et al. (2003) advised a ratio of nine parts water for one part alcohol. The effectiveness of this extractive column on each congener i is related to its relative volatility ( i,ethanol ) to ethanol. The rectifying column C40 (sixty-seven trays) was used to concentrate the diluted alcohol of the C30 bottom stream (B30) and to eliminate remaining higher alcohols mainly propan-1-ol, 2-methylpropan-1-ol and 3 _ methylbutan-1-ol. Two side streams, located above the feed tray, were used to extract higher alcohols: the high side draw (HD40) and the low side draw (LD40). As the high volatile congeners not eliminated previously concentrated at the top of the C40 column, a small flux was withdrawn from the head (H40) and neutral alcohol (NA) was withdrawn a few trays below the top and sent to the C50 column. In the plant, the H40 flux is recycled to the raw alcohol tank. In the C50 demethylising column (seventy-seven trays), the methanol content was reduced by stripping at the head of the column (H50). The neutral spirit was recovered at the bottom. In order to minimize ethanol losses, the unit included the congeners concentrating column C60 (sixty trays). It received all the congener-containing streams coming from the other columns, except the H40 stream that returned to the raw alcohol tank. This column was designed to concentrate and eliminate the congeners with a minimum ethanol loss and to recycle, by stream R30, part of the ethanol back to the extractive column C30. The head stream (H60) and high side product stream (HD60) constituted a low grade alcohol sold as a by-product or burned as fuel. The low draw (LD60), which was highly concentrated in 3methylbutan-1-ol, was sent to the decanter S60, where water (DW60) was added to obtain liquid phase splitting. The decanted fusel oils (FO) may be sold as a byproduct and the water phase (WP) was recycled into the C60 column. The low side draw (LD20) from the distillation column receiving the wine was added to the water phase before it was recycled into the C60 column.

The rectifying plant had an average capacity of 1,500 hL of pure alcohol per day (6.7 m 3 /h of raw alcohol at 93% v/v). It worked in triple effects; only C40 and C60 are heated by steam by a reboiler. The C30 column was heated by part of the C40 vapour and the C50 column was heated by the head vapor of the C30 column. The Optimisation of this process was complex because of the numerous liquid connections and the energy integration between the columns. It was decided to focus the work on the liquid connections. The objective of the simulation of the rectifying plant was to obtain a better understanding of the behaviour of the congeners with regard to reducing the loss of alcohol into the low grade alcohol stream without reducing the neutral spirit quality.

Data acquisition in the neutral spirit plant

The first step of the study was the definition of a nominal point, representative of the steady state operation of the unit, in order to validate the model. Initially, all the circuits and sensors of the plant were checked. The online measurements of pressures, temperatures and flow rates were registered and their hourly averages saved. Some flow rates not registered (LD20, H40, HD40, LD40, H50, H60, HD60, Table 1. Experimental errors were smaller than 2% except for methanol which displayed a relative error of 4.4%.

Table 1

In order to characterise the nominal point of the rectifying plant, a measurement Measured values and analyses are never completely accurate and there are multiple causes of error. The aim of data reconciliation is to generate a statistically coherent data set from the raw data set and to detect gross errors and possible sensor faults [START_REF] Vrielynck | Les procédés mieux maîtrisés grâce à la réconciliation de données[END_REF]. The raw data are adjusted such that the reconciled data satisfy all the mass balances. However, corrections must be coherent with the confidence intervals of the measured data [START_REF] Mandel | Data reconciliation by inequality balance equilibration: a LMI approach[END_REF]. The reconciliation is an optimisation problem within the constraint of the mass balance. The criterion to minimise is the sum of squares of the differences between the measured and corrected values. Relative differences instead of absolute values were chosen due to the large range of flow rate values.

Data reconciliation was initially applied just to satisfy ethanol and total mass balances, using measured total flow rates and ethanol concentrations, and acting on total flow rates only. It seemed important to favour the mass balance of ethanol before considering the other congeners in lower concentrations. Then, the reconciled total flow rates were used for the reconciliation of the mass balance of the congeners by acting on their respective partial flow rates. Relative differences between the measured and reconciled values for ethanol are displayed in Table 2. Except for the H50 and WP+LD20 streams, the reconciled values were in good agreement with the measured values (small differences).

Table 2

A c c e p t e d M a n u s c r i p t 9/9

Relative differences between the measured and reconciled values for the flow rates of the congeners are displayed in Table 3. Except for some particular compounds and flows, the reconciled values have acceptable differences with the measured values.

Table 3 2.3. Simulation procedure with ProSimPlus ®

Thermodynamic models

The choice of the thermodynamic model for the representation of phase equilibria is fundamental for a reliable process simulation. As the entire process was at low pressure (< 2.5 bar), the vapour phase was considered as an ideal gas and the NRTL activity coefficient model [START_REF] Renon | Local compositions in thermodynamic excess functions for liquid mixtures[END_REF]) was chosen to represent the non-ideality of the liquid phase. This model is recommended by Faundez and Valderrama (2004) and Valderrama et al. (2012a). The binary interaction parameters of the binary ethanol-water mixture were estimated from the vapour-liquid equilibrium data of [START_REF] Pemberton | Thermodynamic properties of aqueous nonelectrolyte mixtures II. Vapour pressures and excess Gibbs energies for water + ethanol at 303[END_REF] and [START_REF] Kolbe | Thermodynamic properties of ethanol + water. I. Vapour-liquid equilibria measurements from 90 to 150°C by the static method[END_REF]. The interaction of ther ternary ethanol-water-congener mixtures were estimated from the data of Heitz (1960), [START_REF] Williams | Vapor-Liquid equilibria of organic homologues in ethanol-Wwater solutions[END_REF] and Dechema data bank (Gmehling et al., 1977). For liquid-liquid equilibrium, the set of binary interaction parameters was estimated by Kadir et al. (2008). The interactions between congeners themselves were neglected.

Software Data

The process model was built using the standard unit modules of ProSimPlus. Then, the chemical compounds and the thermodynamic model were selected from the standard chemical compounds database and the library available in ProSimPlus.

Interaction coefficients determined previously were introduced. Simulation was conducted in three steps. First, each column of the industrial plant was modelled separately taking into account the number of real trays, the place of each feed and side draw and the reconciled flow values, and the heat exhanged in the exchangers (reboiler and condenser). Considering only ethanol and water, the efficiencies of the 4 for ethanol. The calculated flow rates were in good agreement with the reconciled ones (less than 2% differences) except for the LD60 and H60 streams. This may be explained by the experimental difficulty in assessing ethanol concentration into flows very rich in higher alcohols.

Table 4

Simulation results of the neutral spirit plant are illustrated in Figure 2 by the ethanol profiles in each column. The trays are numbered from the bottom to the top, as practised in the plant. For the extractive column C30, it can be observed that the concentrations under the feeds (F30 and R30+DW90) were constant until the third tray on the reboiler. This area of constant concentration was christened as the pinch zone by [START_REF] Unger | Production of light-bodied rum by an extractive distillation process[END_REF] who indicated that as the ethanol concentration is reduced as the congeners' extraction is improved. Above the feed, there was a significant decrease of ethanol concentration due to the huge feed of water at the top of the column. Owing to this phenomena, the Murphree's efficiencies of the trays above the feed were reduced to 0.29 instead of 0.7 for the other trays of the column.

Figure 2

Functional analysis of the congeners concentrating column

The column C60 was very important because the congeners are definitely removed from the system: directly via the head (H60) and the high side stream (HD60, tray 25)

and indirectly via the low side stream (LD60, tray 15) sent to the decanter S60 where recycle stream fed to tray 10 of the C60 column induced a small increase in ethanol concentration. As the number of trays below this feed is small, it induced a loss of ethanol in the bottom stream (B60); a mass fraction from 0.0016 to 0.0032 was measured. It may also be observed that the higher alcohols were spread on both sides of the feed tray (Figure 3) and consequently, there was a lower extraction with the low (LD60) and high side streams (HD60). We tested by simulation the outcome of gathering all the feed streams (H30, HD40, LD40 and WP+LD20) on tray 16 and shifting the low side stream (LD60) to tray 18 instead of 15 (Figure 4). These modifications, implemented on the industrial plant, led to a more stable operating mode and a reduction of the ethanol loss. Due to the simulation results, it was possible to convince operators to perform these modifications. Finally, optimisation variables taken into account were only the more sensitive ones (DW30, HD40, H50, H60 and HD60). Optimisation flows are summarised in Table 5.

The optimal point moved significantly from the nominal one (variation of more than 30% for some flux). It can be observed that the demineralised water stream fed into the extractive column (DW30) was increased by 18.7%, which allowed a better extraction of the congeners into the distillate. As the flows extracted from the congeners concentration column, H60 and HD60, decreased (30.5% and 36.2% respectively), the production of low grade alcohol decreased, allowing a productivity gain of 0.83% calculated with the sales price of neutral spirit and low grade alcohol.

Table 5 and 6

Optimisation also achieved a reduction of the concentration of propan-1-ol in the neutral spirit (from 77.3 ppm to 5 ppm), which is proof of better quality (Table 6). The evolution of the optimisation variables was coherent with the sensitivity analysis.

The operating conditions were modified in small increments towards achieving the optimal set points and this work is in progress.

Modelling of the distillate from orange peels

Bitter orange distillate batch distillation plant

The bitter orange distillate production took place in a French beverage company. The four copper stills in the plant (Figure 6) were composed of a boiler (load of 72 hL), a column with six capped trays and a total cooling condenser and cooler feed with cold water at 12 °C. The raw material that was distilled was composed of the flavedo of bitter orange peels macerated for three weeks in neutral alcohol diluted at 30% v/v plus the heads and tails cuts from the previous distillation and water to cover the peels. The mass of liquid was around 5.4 tons per distillation.

An entire distillation took approximately ten hours. The first drops of distillate appeared at the end of one hour of heating. Three different fractions of distillate were separated according to either the ethanol concentration, or the cumulative distillate volume. The split criterion was based on the empirical skills of the distillery. The first fraction is the heads cut, which is in fact separated into two fractions: the first twenty litres were decanted to discard the light phase and to recycle the heavy phase to the heads receiver and the following volume was directly sent to the heads receiver. The second fraction was the heart cut named bitter orange distillate, which corresponded to the most pleasant fraction. This heart cut was the aromatic ingredient added into the bitter orange spirit. The third fraction was the tails cut collected in the same receiver as the heads, to be recycled in the following distillation. After cooling, the still was emptied, rinsed with water and recharged for the subsequent distillation the following morning.

Data acquisition of the bitter orange distillate plant

The study was conducted on one alembic constructed in 1990 by Prulho, France First, all the circuits and sensors were checked. The alembic is equipped with a series of sensors. In the cooling distillate, there was a densitimeter (7830 from Schlumberger, Germany) a temperature sensor (Pt100) and flow meter (Heliflux Faure-Hermann H05INCT). On the reflux line there was a flow meter (Heliflux Faure-Hermann, H2INCT). In the vapour in the swan neck, there was a temperature sensor (Pt100) and pressure gauge (OTI). For the heating steam, there was a pressure gauge (OTI series Z). The calculator assessed from the measurements on distillate flow the ethanol concentration (in %v/v) at 20 °C, the cumulative distillate volume and total ethanol volume. The data acquisition system of the plant was designed to record the parameters every six minutes. Three distillation runs were followed. Nine distillate samples of 100 mL were collected in glass bottles over each distillation: three samples at the beginning, the middle and the end of each cut. For the purpose of this study, a specific tank was added to collect the two fractions of heads separately. As heads cut samples separate into an oil phase rich in terpene and an aqueous phase, these samples were diluted in absolute ethanol for analysis. All samples were stored at -20 °C.

In a previous study, Deterre et al. (2012a) detected and quantified the volatile compounds of a heart cut and characterised their odour by using GC-Olfactometry. In total, forty molecules were detected in the heart cut and among them seven were selected by nine trained panellists using the GC-olfactometry and frequency method.

For this study, four molecules were selected because of their concentration or their 7.

Table 7

It can be seen that sometimes the standard deviations were high compared with the averages. According to Can [START_REF] Baser | Handbook of essential oils Science, Technology and Applications[END_REF], this was due to the fact that the essential oil produced by distillation with an alembic can have a very variable composition at 8.

Table 8

Software Data

The structure of the alembic (column with six capped trays and total condenser), the receivers to collect the three cuts (heads, heart and tails) and the control parameters were chosen according to experimental data using BatchColumn software. As the condenser and the capped trays of the column contained liquid before start-up (from the previous run), their volumes and ethanol composition were measured in the industrial plant and registered. The temperature of the condensed vapour (reflux and distillate) was fixed at 20 °C and the pressure drop of the column at 18 mbar. The mass of the charge (5,462 kg) in the pot still, calculated from mass balance into the plant, is reported in Table 9. One distillation run was represented by a sequence of different operating steps. An initial step, called the filling step, was created to represent the alembic heating phase. The initial temperature of the charge was fixed at 25 °C and the heat flux was fixed at 372 kW in order to represent the time necessary to reach the boiling point of the charge (one hour). According to experimental data, appropriate events for each step were programmed to take into account the variation of distillate flow rate and reflux ratio set points over time (Figure 7) and to separate the cuts (Table 10). It was difficult during the distillation of the tails to represent precisely the continuous variation of the distillate flow rate and the reflux ratio despite the five steps used to represent this task.

Figure 7

Table 10 As in the previous study, tray efficiencies were modified to obtain good agreement of the profile of distillate ethanol concentration over time with experimental data.

Efficiencies were chosen at 0.6, except for the highest tray, which was set at 0.4 because of the reflux flow that arrived at 20 °C.

Comparison of simulation results with experimental data

Figure 8 compares the simulation results and experimental ethanol concentration profiles in the distillate. Table 11 compares the characteristics (duration, volume and ethanol volume) of the three cuts.

Figure 8

Table 11 The experimental and simulated values were in very good agreement for the heart cut, which is the most important cut. As the heads run in a very short period, it was difficult to adjust the simulation parameters accordingly. Despite the difficulty to Then, the behaviour of the four aroma compounds was observed. The aim was to compare the profiles of the four compounds, particularly if they were detected in the same periods of time (Figure 9 and Figure 10).

Figure 9

Figure 10 Regarding the mass fractions of the four aroma compounds, it can be concluded that their behaviour was well represented by the simulation in comparison with the actual distillation carried out in the distillery. The two monoterpene hydrocarbons (Figure 9)

were also detected in the heads cut and the oxygenated compounds (Figure 10) in the tails cut. Concerning the monoterpene hydrocarbons and the linalool profiles, we observed that they were detected for a longer period of time in the experimental profiles compared with the simulated profiles. This was because the software does not simulate the effect of the peels present in the boiler. Indeed, it was demonstrated by experimental distillation without peels that they do not immediately release the limonene they contain and thus delay its distillation.

Moreover, as the parameters of the chemical reactions were unknown, the linalool oxide profile (from linalool degradation) was simulated by adding linalool oxide in the initial charge of the boiler (3.10 -5 in mass fraction). A time difference between the simulated and experimental profiles was also observed, which was due to the peels being in the boiler and the fact that the linalool degradation reaction did not take place in the simulation.

All these results confirm the choice of the thermodynamic model to represent the aroma compounds. We concluded that the simulation results were correct from a quantitative as well as a qualitative point of view.

A c c e p t e d M a n u s c r i p t 18/18

Conclusion

Concerning the production of neutral spirit, the model validation required a precise functional analysis of the plant. Thus, all circuits and sensors were first checked and several problems were corrected. Then, the reconciled mass balances were generated from raw data as reference. Simulation results were very satisfactory.

Simulation enables the visualisation of all the congeners' profiles and thus facilitates the understanding of their behaviours and suggestions for improvements.

Modifications implemented for the low grade concentration column (C60) allowed much better operation. Due to the optimisation, changes resulting in maximal recovery of ethanol into spirit were indicated.

Concerning the production of the heart cut from macerated orange peels distillation, a correct modelling procedure of pot still distillation allowed for improvements in the selection of distillate cuts, considering the product quality, the maximal recovery of ethanol in the heart fraction (spirit) and the energy consumption.

Results presented in this paper demonstrate that it is possible to simulate the operation of continuous and batch beverage distillation plants using commercial process simulation software. The use of validated process simulation is a powerful tool to analyse the operation of a plant and to modify plant operating parameters in order to increase both product quality and efficiency.

B40

(1) B60

(1)

(1) fixed values for simulation Mean of the mass fractions ± standard deviation Cut α-pinene (x 10 -5 ) D-limonene (x 10 -3 ) Linalool (x 10 -4 ) Linalool oxide (x 10 -5 )

  recommended the use of a simplified model even if it was less accurate. It is also possible to use commercial simulators. Claus and Berglund (2009) used the ChemCad batch distillation program to model the batch multistage distillation still used to produce fruit spirits. They conducted experiments on the lab-scale (10 L and 150 L stills) and adjusted the simulation parameters to model the distillate flow rate, reflux ratio and ethanol concentration. The objective of the present work was to check the ability of ProSim® software to model both continuous and batch distillations of two specific industrial units in order to obtain both a better understanding of the behaviour of aroma volatile componentsA c c e p t e d M a n u s c r i p t 5/5 and a a tool to optimise the stills' operation. The first part of the present work follows the study of Decloux and Coustel (2005) as it validated the modelling with ProSimPlus software of an industrial plant producing neutral spirit from raw alcohol.

  the distillation plant, including the distillation of the wine to produce the raw alcohol, was 19.63 ton/h (310 kg of steam per hL of pure alcohol).

  LD60 and DW60) were taken at the time of sampling, which was every hour for eight hours to verify the steady state. Ethanol concentration of the samples in volume percentage (% v/v) was measured with an Anton Paar DMA5000 densitometer, according to the OIV (1994) method. For mass balance, a correlation between ethanol concentration in volume percentage and density was established from Oudin (1980) data. Analyses of the ten selected congeners were carried out with a Varian 3800 series gas chromatograph with the following specifications: capillary column, CP-WAX 57 CB, 50 m  0.32 mm  0.2 µm directly interfaced with a flame ionisation detector. The carrier gas was hydrogen at 2 mL/min. Injection was carried out in split mode 1/50 with an injection volume of 1 L. Temperature of the injector was 210 °C and that of the detector was 240 °C. The temperature program was 4 min at 35°C, then increased by 4.5 °C/min up to 90 °C and then increased by 15 °C/min up to 130 °C. Data were analysed with the Varian WorkStation program. To obtain quantitativeresults, the internal standard method was applied with 4-methylpentan-2-ol[START_REF] Tranchant | Manuel pratique de chromatographie en phase gazeuse[END_REF] and six calibration mixtures were prepared. Each sample was analysed in duplicate. Every ten samples, a solution of known concentrations was analysed for quality control. The relative errors between the known concentrations of this solution and the quantified concentrations are displayed in

  campaign was carried out during eight hours of stabilized operating point. Every hour, 50 mL of each flux was sampled in order to constitute a representative sample at the end. During the same time, each value of the sensors not digitally recorded was manually read. The concentrations of ethanol and congeners were assessed for the twenty three fluxes sampled. Three series of data collection were carried out.

  to obtain good agreement between the experimental data and simulation results. In the second step, modelling was conducted taking into account the streams integration between the columns. Finally, the ten congener compounds analysed in the experimental part were added into the feeds while maintaining all the other parameters at their previous values.2.4. Simulation of the neutral spirit plant2.4.1. Comparison of simulation results with experimental dataComparison between the simulation results of each column separately and the reconciled values are given in Table

  fusel oils (FO) are removed. It can be observed in Figure2that the (WP+LD20)

Figure 3 Figure 4 2

 34 Figure 3

  Figure 5

Figure 6 A

 6 Figure 6

  odour impact: two monoterpene hydrocarbons (-pinene and D-limonene) and two oxygenated hydrocarbons (linalool and linalool oxide). Analyses of the aroma compounds plus ethanol were carried out with an Agilent Technologies 6890 GC and an apolar capillary DB-5 (5%diphenyl/95%dimethyl siloxane) column 30 m long, 0.320 mm I.D. and 0.5 µm film thickness, directly interfaced with a flame ionisation detector and helium carrier gas (flow rate 1 mL/min). The temperatures of the injector and the detector were 240 °C. For all components except limonene, injections of 1 L were made in splitless mode and the temperature program was 1 min at 50°C then increased by 10 °C/min up to 80°C, then increased by 3 °C/min up to 130 °C, then increased by 5 °C/min up to 240 °C and finally increased by 7°C/min up to 270 °C, hold 3 min. For limonene injections of 1 L were made in split mode (1/50) and the temperature program was 1 min at 50 °C then increased by 10 °C/min up to 80 °C, then increased by 3° C/min up to 120 °C, and finally increased by 20 °C/min up to 270°C, hold 3 min. The software MSD ChemStation Data Analysis (Rev D) from Agilent was used for control, general operations and data acquisition of the results. For purposes of quantification, internal standard solutions of nonane and tridecane were added. A mixture of four aroma compounds of bitter orange was prepared with distilled water and absolute ethanol as solvents. Response factors of each compound were thus determined. All samples were analysed three times. Averages and standard deviations of the mass fractions of the four aroma compounds over the three distillations of all cut samples are displayed in Table

  Despite theses variations, it can be observed that terpene molecules (-pinene and D-limonene) went out in the first fraction of distillate (heads); whereas ,linalool and linalool oxide arrive in the last fraction (tails). It was checked whether the presence of linalool and linalool oxide into the first fraction was due to liquid remaining in the dead volume from the previous distillation. 3.3. Batch distillation modelling with BatchColumn 3.3.1. Thermodynamic models A previous study by Deterre et al. (2012b) reported vapour-liquid equilibrium measurements of the four compounds (-pinene, D-limonene, linalool and linalool oxide) in alcoholic solutions of different ethanol concentrations and the choice of the thermodynamic model. The water-ethanol VLE data were assessed with the NRTL model and the binary interaction parameters available in the ProSim databank because the parameters fitted during the neutral spirit study were the property of the industrial partners. However, results obtained with these two sets of binary interaction parameters were similar. The four aroma compounds were represented by a Henry's law type model by Deterre et al. (2012b) as it gave the best representation of experimental data. Parameters are reported in Table

  variation of the parameters during the tails, a quantitative match between experimental and simulation data was mostly attained. Moreover the volumes remaining in the pot still at the end of the distillation in the plant(3,563 L) and from the simulation (3,865 L) were close.

  This study proves interest of simulation to optimise neutral spirit distillation plant Neutral alcohol quality was improved and yield increased Simulation explains aroma compounds behaviour in bitter orange distillate production Both industrial applications included selection of compounds and process balance Simulations included selection or measurement of L/V or L/L equilibria data A c c e p t e d M a n u s c r i p t

  Table7-Averages and standard deviations of mass fractions of the aroma compounds over three distillations in the three cuts of distillate (nd as not detected).

Fig. 1 -Fig. 3 -Fig. 4 -Fig. 5 -Fig. 8 -Fig. 9 -Fig. 10 -

 13458910 Fig.1 -Process diagram of the neutral spirit plant.

FigureFig. 1 -

 1 Figure

Fig. 2 -Fig. 3 -

 23 Fig. 2 -Plots of ethanol concentration as a function of real stage number for (a) C30, (b) C40, (c) C50 and (d) C60. Plot (e) shows a magnified section of plot (d). Discrete points represent solutions calculated on each stage with lines joining data points used for visual guidance only.

Fig. 4 -

 4 Fig. 4 -Profiles of the concentration of ethanol (left side) and congeners (right side) in the congeners concentrating column C60 with all feeds H30 + HD40 + LD 40 +WP+LD20 on tray 16 and extraction of LD60 on tray 18.

Fig. 5 -

 5 Fig. 5 -Variation of (a) neutral spirit quality and (b) profit gain variation as a function of the percentage variation of key flow rates from their nominal value.

Fig. 9 -Fig. 10 -

 910 Fig. 9 -Comparisons of α-pinene (a) and D-limonene (b) behaviours of three trials (data points) vs. the simulation model (solid line). Dotted lines indicate separation of cuts by simulation.

Table 9 A

 9

	16/16
	M a n u s c r i p t
	c c e p t e d

Table 4 -

 4 Relative differences (%) between reconciled data and simulation results for ethanol mass flow rates.

	Flow	(%)	Flow	(%)	Flow	(%)	Flow	(%)
	H30	(1)	H40	0.52	H50	0.71	H60	1.67
					Neutral
	F30	(1)	NA	0.16	spirit	-0.04	HD60	10.3
	R30	1.39	HD40	-2.18			LD60	-50.0
	B30	0.00	LD40	0.00			WP+LD20	(1)

Table 5 -

 5 Comparison of the initial fluxes before optimisation (nominal) and after.

		Before	After
	optimisation	optimisation
		Flow (kg.h -1 )	Variation (%)
	DW30	56,800		67,400	18.7
	HD40	538		700	30.1
	H50	290		400	38.0
	H60	128		89	-30.5
	HD60	141		90	-36.2
	Table 6 -Comparison of concentrations of ethanol and propan-1-ol before (nominal) and
	after optimisation.
	Concentration			Before	After
	in neutral spirit	optimisation	optimisation
	propan-1-ol (ppm)	77.3	5.0
	Ethanol (w/w)		0.9415	0.9425

Table 8 -

 8 Henry's Constants A′ and B′ (from equation ln Hi=A′ + B′/T) of the aroma expressed in Pa with respect to the aroma mole fraction (Deterre et al., 2012b).

		Beginning	5.6 ± 3.2		9.1 ± 6.5	1.7 ± 0.3	4.1 ± 0.6
	Heads	Middle	55.7 ± 10.2	60.6 ± 5.6	1.0 ± 0.02	3.9 ± 0.2
		End	108 ± 24		102.0 ± 9.1	0.2 ± 0.02	0.7 ± 0.1
		Beginning	64.2 ± 24.1	64.4 ± 11.3	0.03 ± 0.006	0.1 ± 0.03
	Heart	Middle	2.5 ± 0.2		3.6 ± 0.3	0.05 ± 0.002	0.2 ± 0.02
		End	1.4 ± 0.2		2.7 ± 0.1	1.1 ± 0.1	6.2 ± 1.1
		Beginning	1.4 ± 0.1		2.6 ± 0.1	1.7 ± 0.1	15.0 ± 2.3
	Tails	Middle	nd		1.4 ± 0.5	3.2± 1.9	86.8 ± 42.6
		End	nd		0.09 ± 0.02	1.5 ± 1.0	38.2 ± 23.9
	compounds	A'	B'
	α-pinene	288.87	-97,207
	D-limonene	305.82	-103,438
	linalool		168.20	-56,072
	linalool oxide	137.23	-45,088

Table 9 -

 9 Initial composition of the charge in the pot still expressed as mass fractions for all

	the compounds.
	Compounds	Mass fractions in the initial charge	Charge in the still (kg)
	Water	0.7899
	Ethanol	0.2087
	α-pinene	1.71x10 -5
	D-limonene	1.27x10 -3	5,462
	Linalool	9.34x10 -6
	Linalool oxide	3x10 -5

Table 10 -

 10 End events and values of distillate flow and reflux ratio for the simulation steps

		Cut	Reflux ratio	Distillate flow (kg.h -1 )	End event of the step
	Step 1	Heads	2.5		160		13.92 kg of distillate
	Step 2	Heads	3.65	170		Ethanol mass fraction in distillate = 0.8000
	Step 3	Heart	3.65	170		Ethanol mass fraction in distillate = 0.8573
	Step 4	Heart	3.56	200		Ethanol mass fraction in distillate = 0.8559
	Step 5	Tails	2.5		198		Ethanol mass fraction in distillate = 0.7000
	Step 6	Tails	1.7		220		Ethanol mass fraction in distillate = 0.4000
	Step 7	Tails	1.2		220		Ethanol mass fraction in distillate = 0.2000
	Step 8	Tails	0.7		260		Ethanol mass fraction in distillate = 0.0500
	Step 9	Tails	0.1		210		Ethanol mass fraction in distillate = 0.0358
	Table 11 -Comparisons between experimental data (exp), simulated ones (sim) and relative
	differences (%)
								Distillate volume of
		Duration		Distillate volume		ethanol
			(h)			(L)			(L)
		exp	sim	%	exp	sim	%	exp	sim	%
	Heads	0.42	0.2 -52.4	68	45 -33.8	48	25 -47.9
	Heart	4.82	4.73 -1.9 1,145	1,171	2.3	1,050	1,047	-0.3
	Tails	3.12	3.4	9.0	721	851 18.0	294	363	23.5
	total	8.36	8.33 -0.4 1,934	2,067	6.9	1,392	1,435	3.1

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Chemistry data series, Francfort. Gmehling, J., Jiding, L., & Schiller M. (1993). A modified UNIFAC model. 2. present parameter matrix and results for different thermodynamic properties. Industrial & Engineering Chemistry Research,32,[178][179][180][181][182][183][184][185][186][187][188][189][190][191][192][193] Haypek, E., Silva, L.H.M., Batista, E., Marques, D.S., Meireles M.A.A., & Meirelles A.J.A. (2000). Recovery of aroma compounds from orange essential oil. 1) B60

Brazilian

(1)

(1) non measured flow rates