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Global manufacturing accounted for 17% of global GDP in 2021. The heavy constructionequipment industry creates significant socio-ecological impacts through CO2 emissions, landdegradation and social risks. It is important to implement sustainability from the early phasesof product development. Research shows lack of cooperation between academia andbusinesses in testing to improve Sustainable Product Development (SPD) tools. The studyidentifies needs for implementing SPD and testing of relevant SPD tools for a heavyconstruction equipment manufacturer. It applies DSIP methodology as theoretical frameworkand focus group interviews / workshops, document content analysis and observation as datacollection methods. SAM4SIP supported in identifying the capability needs in relation toSPD implementation and informed the selection of the two SPD tools to be tested at the casecompany. First, the Leading Sustainability Criteria (LEASA) workshop generated 10measurable criteria covering all product life cycle phases which thereafter were furtherdeveloped in the Overall Sustainability Fingerprint template with respective compliancelevels to create design space. The results emphasize on the importance of taking a full-systemperspective to implement SPD on all decision levels of a company and giving opportunity tomanufacturers to utilise DSIP and find suitable tools to implement SPD.

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<p>In this dissertation, numerical analysis frameworks and software tools for digital design of process systems are developed. More specifically, these tools have been focused on digital design within the pharmaceutical manufacturing space. Batch processing represents the traditional and still predominant pathway to manufacture pharmaceuticals in both the drug substance and drug product spaces. Drug substance processes start with raw materials or precursors to produce an active pharmaceutical ingredient (API) through synthesis and purification. Drug product processes take this pure API in powder form, add excipients, and process the powder into consumer doses such as capsules or tablets.  Continuous manufacturing has allowed many other chemical industries to take advantage of real-time process management through process control, process optimization, and real-time detection of off-spec material. Also, the possibility to reduce total cleaning time of units and encourage green chemistry through solvent reduction or recycling make continuous manufacturing an attractive alternative to batch manufacturing. However, to fully understand and take advantage of real-time process management, digital tools are required, both as soft sensors during process control or during process design and optimization.  Since the shift from batch to continuous manufacturing will proceed in stages, processes will likely adopt both continuous and batch unit operations in the same process, which we will call {\em hybrid} pharmaceutical manufacturing routes. Even though these processes will soon become common in the industry, digital tools that address comparison of batch, hybrid, and continuous manufacturing routes in the pharmaceutical space are lacking. This is especially true when considering hybrid routes. For this reason, PharmaPy, an open-source tool for pharmaceutical process development, was created to address rapid in-silico design of hybrid pharmaceutical processes.  Throughout this work, the focus is on analyzing alternative operating modes within the drug substance manufacturing context. First, the mathematical models for PharmaPy's synthesis, crystallization, and filtration units are discussed. Then, the simulation capabilities of PharmaPy are highlighted, showcasing dynamic simulation of both fully continuous and hybrid processes. However, the technical focus of the work as a whole is primarily on optimization techniques for pharmaceutical process design. Thus, many derivative-free optimization frameworks for simulation-optimization were constructed and utilized with PharmaPy performing simulations of pharmaceutical processes.  The timeline of work originally began with derivative-based methods to solve mixed-integer programs (MIP) for water network sampling and security, as well as nonlinear programs (NLPs) and some mixed-integer nonlinear programs (MINLPs) for design space and feasibility analysis. Therefore, a method for process design that combines both the ease of implementation from a process simulator (PharmaPy) with the computational performance of derivative-based optimization was implemented. Recent developments in Pyomo through the PyNumero package allow callbacks to an input-output or black-box model while using {\sc Ipopt} as a derivative-based solver through the cyipopt interface. Using this approach, it was found that using a PharmaPy simulation as a black box within a derivative-based solver resulted in quicker solve times when compared with traditional derivative-free optimization strategies, and offers a much quicker implementation strategy than using a simultaneous equation-oriented algebraic definition of the problem.  Also, uncertainty exists in virtually all process systems. Traditionally, uncertainty is analyzed through sampling approaches such as Monte Carlo simulation. These sampling approaches quickly become computational obstacles as problem scale increases. In the 1980s, chemical plant design under uncertainty through {\em flexibility analysis} became an option for explicitly considering model uncertainty using mathematical programming. However, such formulations provide computational obstacles of their own as most process models produce challenging MINLPs under the flexibility analysis framework.  Specifically when considering pharmaceutical processes, recent initiatives by the FDA have peaked interest in flexibility analysis because of the so called {\em design space}. The design space is the region for which critical quality attributes (CQAs) may be guaranteed over a set of interactions between the inputs and process parameters. Since uncertainty is intrinsic to such operations, industry is interested in guaranteeing that CQAs hold with a set confidence level over a given operating region. In this work, the {\em probabilistic design space} defined by these levels of confidence is presented to address the computational advantages of using a fully model-based flexibility analysis framework instead of a Monte Carlo sampling approach. From the results, it is seen that using the flexibility analysis framework decreased design space identification time by more than two orders of magnitude.  Given implementation difficulty with new digital tools for both students and professionals, educational material was developed for PharmaPy and was presented as part of a pharmaceutical API process development course at Purdue. The students were surveyed afterward and many of the students found the framework to be approachable through the use of Jupyter notebooks, and would consider using PharmaPy and Python for pharmaceutical modeling and data analysis in the future, respectively.  Through software development and the development of numerical analysis frameworks, digital design of pharmaceutical processes has expanded and become more approachable. The incorporation of rigorous simulations under process uncertainty promotes the use of digital tools in regulatory filings and reduces unnecessary process development costs using model-based design. Examples of these improvements are evident through the development of PharmaPy, a simulation-optimization framework using PharmaPy, and flexibility analysis tools. These tools resulted in a computational benefit of 1 to 2 orders of magnitude when compared to methods used in practice and in some cases reduce the modeling time required to determine optimal operating conditions, or the design space of a pharmaceutical manufacturing process.</p>

Scientific Computing (SC) software has significant societal impact due to its application in safety related domains, such as nuclear, aerospace, military, and medicine. Unfortunately, recent research has shown that SC software does not always achieve the desired software qualities, like maintainability, reusability, and reproducibility. Software Engineering (SE) practices have been shown to improve software qualities, but SC developers, who are often the scientists themselves, often fail to adopt SE practices because of the time commitment. To promote the application of SE in SC, we conducted a case study in which we developed new SC software. The software, we developed will be used in predicting the nature of solidification in a casting process to facilitate the reduction of expensive defects in parts. During the development process, we adopted SE practices and involved the scientists from the beginning. We interviewed the scientists before and after software development, to assess their attitude towards SE for SC. The interviews revealed a positive response towards SE for SC. In the post development interview, scientists had a change in their attitudes towards SE for SC and were willing to adopt all the SE approaches that we followed. However, when it comes to producing software artifacts, they felt overburdened and wanted more tools to reduce the time commitment and to reduce complexity. While contrasting our experience with the currently held perceptions of scientific software development, we had the following observations: a) Observations that agree with the existing literature: i) working on something that the scientists are interested in is not enough to promote SE practices, ii) maintainability is a secondary consideration for scientific partners, iii) scientists are hesitant to learn SE practices, iv) verification and validation are challenging in SC, v) scientists naturally follow agile methodologies, vi) common ground for communication has always been a problem, vii) an interdisciplinary team is essential, viii) scientists tend to choose programming language based on their familiarity, ix) scientists prefer to use plots to visualize, verify and understand their science, x) early identification of test cases is advantageous, xi) scientists have a positive attitude toward issue trackers, xii) SC software should be designed for change, xiii) faking a rational design process for documentation is advisable for SC, xiv) Scientists prefer informal, collegial knowledge transfer, to reading documentation, b) Observations that disagree with the existing literature: i) When unexpected results were obtained, our scientists chose to change the numerical algorithms, rather than question their scientific theories, ii) Documentation of up-front requirements is feasible for SC We present the requirement specification and design documentation for our software as an evidence that with proper abstraction and application of “faked rational design process”, it is possible to document up-front requirements and improve quality. / Thesis / Master of Science (MSc)

Denna rapport återger det arbete som syftat till att ta fram hjälpmedel för att utveckla hållbara förpackningslösningar. Projektet har genomförts som ett examensarbete för högskoleingenjörs- programmet i innovationsteknik och design vid fakulteten för hälsa, natur- och teknikvetenskap på Karlstads universitet. Arbetet har realiserats med stöd från Helén Williams, forskare inom energi- och miljöteknik. I dagsläget saknas metoder och hjälpmedel för att utveckla hållbara livsmedelsförpackningar där matsvinn tas in som en aspekt. Därför har en serie med tre verktyg utvecklats, från befintlig designprocessmetodik för tillämpning vid produktutveckling av hållbara förpackningslösningar, med fokus på reducerat matsvinn och minskad miljöpåverkan. För att få underlag till utvecklingen av verktygen har en förstudie genomförts där designprocess, hållbar utveckling, förpackning och matsvinn studerats. Förstudien har följts av en analys för att kartlägga var i produktutvecklingsprocessen verktyg för hållbar utveckling saknas. Detta för att identifiera faser med behov av fler hjälpmedel och resurser. Vidare har de olika faserna studerats mer ingående och det har lett fram till det slutliga resultatet bestående av verktygsmallar och riktlinjer för funktionsanalys, kvalitetshus och Pugh’s metod för reducerat matsvinn. För att sätta den centrala delen, kvalitetshusmallen, i ett verklighetsperspektiv har ett test utförts. Objektet för testet är tre olika varianter av mjölkförpackningar. Verktygen är ämnade till förpackningsdesigners och förpackningsteknologer för att underlätta deras arbete och vägval mot mer hållbara lösningar genom ett minskat produktsvinn. Att verktygen är enkla och användarvänliga är ett mål och en förutsättning för att de ska komma till användning. / This report covers a process that has aimed to contribute with tools for development of sustainable packaging solutions. The project has been implemented as a Bachelor thesis for the programme in  innovation and design engineering at the faculty of health, science and technology at Karlstad university. The project has been carried out with support from Helén Williams, researcher of  environmental and energy systems. Today there is a lack of methods and tools for developing sustainable packaging solutions, in which food waste is taken in as an aspect. A set of three tools has therefore been elaborated from existing methods. The set can be applied in the designprocess of sustainable packaging solutions to achieve reduced food waste and environmental impact. The feasibility study of the project has aimed to provide information for support and included the keywords: design process, sustainable development, packaging and food waste. After the feasibility study, an analysis has been made. The intention has been to map where tools for sustainable development are missing. This would help to identify the phases of the design process with extra need of methods and tools. The analysis has given an opportunity to study the selected phases more thoroughly. That has led to the final result consisting of templates and guidelines for function analysis, QFD and Pugh’s method for reduced food waste. A test has been performed to put the central part, the QFD-template, into a reality perspective. The objects for the test consists of three types of milk packages. The tools are aimed to be used by package designers and packages technologists to facilitate their work and decisions to more sustainable solutions through reduced product waste. The goal and the prerequisite are that the tools should be easy and user friendly. If not there is a risk that they might not be used.

L'activité de conception est un processus complexe et décisif dans le cycle de vie des produits et des procédés de fabrication. Dans le contexte actuel, les chercheurs et ingénieurs de conception notent une nette augmentation de la complexité des produits et procédés, pour satisfaire au mieux l’ensemble des exigences croissantes provenant de l’ensemble des acteurs du cycle de vie (industriels et utilisateurs) mais aussi du monde normatif. La gestion des connaissances et de l’expertise métier est un atout important pour rendre plus efficace et accélérer ce processus. Les recherches actuelles sur la gestion des connaissances font émerger des méthodes et outils performants pour identifier, formaliser, exploiter et diffuser la connaissance et les expériences issues de conceptions passées en vue de produire rapidement de nouvelles solutions. Parmi les approches existantes le Raisonnement à Partir de Cas (RàPC) et la Programmation Par Contraintes (PPC) correspondent aux besoins identifiés en Génie des Procédés. A partir de l’analyse de ces deux approches, ce travail propose un couplage du RàPC et de la PPC afin de fournir un cadre méthodologique et un outil logiciel pour une aide à la conception. Le RàPC permet de capitaliser et de remémorer les expériences passées. Toutefois, la modification de la solution passée pour répondre aux exigences du nouveau problème nécessite l’ajout de nouvelles connaissances aussi appelées connaissances d’adaptation. La PPC, quant à elle, offre justement un cadre approprié pour modéliser et gérer la connaissance permettant l’obtention d’une solution à un problème mais aussi ces connaissances d’adaptation. Outre la formalisation des connaissances d’adaptation, une des difficultés réside dans l’acquisition de ces connaissances. Dans l’approche proposée, le cycle traditionnel du RàPC a été modifié de façon à créer une boucle d’interaction avec l’utilisateur. Lorsqu’un échec d’adaptation se produit, cette boucle est activée et l’expert est sollicité pour apporter les modifications nécessaires à l’obtention d’une solution appropriée. Cette correction est l’occasion d’acquérir en ligne cette nouvelle connaissance, qui sera par la suite mise à jour et ajoutée dans le système. Un cas d’étude sur la conception d’une opération unitaire de génie des procédés permet d’illustrer l’approche. / Design is a complex and crucial process within the lifecycle of products and production processes. In the current context, design engineers and researchers notice an increasing in complexity of products and processes, in order to meet all the requirements coming from all the participants(manufacturers and users alike) in the life cycle and in the normative world as well. Knowledge management is an important asset to accelerate this process and improve its efficiency. Current research on knowledge management is producing new methods and tools to identify, formalize, exploit and disseminate knowledge from past designs experiences to produce new solutions rapidly. Among existing approaches, Case-Based Reasoning (CBR) and Constraint Programming (CP) are suited to needs identified in Process Engineering. Based on the analysis of these two approaches, this work proposes a coupling of CBR and the CP to provide a methodological framework and a software tool to assist design. The CBR allows to capitalize and retrieve past experiences. However, transforming the past solution to fit the new problem requirements needs the addition of new knowledge also known as Adaptation Knowledge. CP, meanwhile, offers an appropriate framework to model and manage knowledge required to obtain an appropriate solution to a problem, but also the adaptation knowledge. In addition to the formalization of adaptation knowledge, one of the remaining major difficulties lies in knowledge acquisition. In the proposed approach, the traditional CBR cycle has been modified to create a user interaction loop. When an adaptation failure occurs, this loop is activated and the expert is asked to make the necessary changes to achieve an appropriate solution. This correction is an opportunity to acquire this new knowledge online, which will be subsequently updated and added into the system. A case study on the design of a unit operation of Process Engineering is used to illustrate the approach

La conception de procédé agroalimentaire est une activité complexe, caractérisée par la grande diversité des produits et des procédés étudiés, ainsi que par la disparité des contextes de production (artisanale ou industrielle). La conception de systèmes de transformation alimentaire adaptés est animée par d'importants enjeux humains, sanitaires, économiques, environnementaux et même culturels. Dans le cas des Pays du Sud, l'explosion démographique et l'urbanisation croissante impliquent de développer un système de production industriel capable de valoriser des produits issus de savoir-faire traditionnels, tout en répondant à des contraintes de coût et de productivité.Pour prévenir toute perte de temps causée par des analyses de type « essai-erreur », et afin d'éviter des coûts de développement superflus, il existe des outils spécifiques à l'analyse décisionnelle multicritères (MCDA) pouvant être déployé dès les phases préliminaires de la conception. En particulier, il est possible d'analyser le potentiel et les limites technologiques d'un concept défini dans un contexte donné, par l'analyse de l'ensemble de solutions les plus performantes, dites Pareto-optimales. Ces solutions se distinguent par les valeurs de leurs variables de conception, qui sont autant de degrés de liberté pour le dimensionnement du concept (géométrie, matériaux, conditions opératoires).Notre cas d'étude concerne l'évaluation d'un concept de fumage à chaud à plaques radiantes, pour la production de poisson fumé, traditionnellement consommé en Afrique Centrale et de l'Ouest. En effet, avant de prétendre à la diffusion de cette technologie déjà brevetée, il faut s'assurer que le procédé puisse satisfaire des objectifs de production et de performances énergétiques, tout en maintenant une qualité du produit satisfaisante. Ainsi, un outil d'optimisation multiobjectif spécifique a été développé en se basant sur la modélisation du comportement du procédé. Une première étude expérimentale a permis de construire un modèle de séchage du poisson dans des conditions d'air variables (température, vitesse et humidité), qui représente à la fois les flux d'évaporation et les flux liés aux écoulements gravitaires de graisses et d'eau. Dans un second temps, un outil de simulation existant a été amélioré afin de représenter des phénomènes ayant un impact significatif sur les performances du procédé, tels que l'aéraulique des fumées, le recyclage de l'air et la régulation thermique. Ainsi, un modèle d'observation a été établi. Il permet de prédire le comportement de différentes solutions possibles, définies par huit variables de conception, et d'évaluer leurs performances sur la base de six variables d'observation.Dans un dernier volet, la formalisation des préférences et de la connaissance experte du procédé permet d'interpréter les performances en termes de désirabilités (satisfaction), qui sont agrégées en un indice de satisfaction global (fonction objectif) par un principe de précaution. Un algorithme génétique permet alors de trouver une solution optimale qui maximise cette fonction objectif, en explorant l'espace des solutions possibles de manière combinatoire. Cette démarche de conception a été fructueuse car elle a permis de proposer un dimensionnement permettant d'obtenir des performances très satisfaisantes. Il a aussi été possible de proposer des améliorations ciblées pour redéfinir le concept actuel du fumoir à plaques. Par ailleurs, il est à noter que le modèle de comportement peut facilement être réadapté pour d'autre type de produits. Dans la perspective d'étendre l'utilisation de cette démarche à d'autres cas d'étude, un effort devra être mené pour la collecte de données fonctionnelles issues de l'expertise. / Food process design is a complex activity, given the wide diversity of existing product and processes, and the plurality of production contexts. Designer must meet the requirements derived from the critical stakes from human, sanitarian, economic, environmental and cultural point of views. In southern countries, the rapid growth of population drives the need of more industrial processes able to valorize traditional products.The savings of development time and extra-expenses are mainly determined by the quality of design choices from the early stage of the designing process, called embodiment design. Multiple criteria decision analysis (MCDA) techniques are used in this purpose, which enable to evaluate and criticize any technological concept. In a specific context, it is possible to generate the Pareto-set of a concept, which is composed of the most efficient possible alternatives. Indeed, every design alternative is defined by some design (or decision) variables which are the degree of freedom for the dimensioning of the system considered. Our case study focuses on a technological innovation to perform hot-smoking using radiant plates (for sanitarian purpose). It is aimed to be developed for the production of traditional hot-smoked catfish widely consumed in West and Central Africa. This is a multicriteria design problem since many objectives have to be satisfied, and concern the product quality, production and energetic performances.In a first work, the mass reduction of catfish dried in hot air conditions was modeled from empirical measurements. In particular, this model takes into account the influence of the drying air conditions (Temperature, Velocity and Relative Humidity) on the calculation of the mass fluxes of evaporation and drips. After that, a global simulation model of the radiant plate hot-smoking process was developed from a previous work. Some key phenomena were described (pressure losses, air recycling, thermal regulation) as they could strongly impact the process performances. The resulting observation model allows predicting the performances of any design alternative defined by a set of 8 design variables.In a final work, expert knowledge and preference were mathematically introduced in a multiobjective optimization tool, meaning some desirability functions. Therefore, every performance variable is converted into desirability indices (traducing the level of satisfaction) and then aggregated into a single global desirability index (thus defining a global objective function). The optimal design of the concept is found using a genetic algorithm.This multiobjective optimization method enabled to find very satisfactory design solution for the radiant plate hot smoking process. More to the point, the analysis of a wide range of Pareto-optimal solutions enabled to better understand what were the strengths and weaknesses, so it was possible to suggest some targeted improvement to the current radiant plate smoking technology. Also, it is noticeable that the current simulation model can be easily adapted to other products. For the purpose of a generalization of the use of such multiobjective methods for the design of food processes, it has been pointed out that efforts should be made to gather expert criteria other relevant functional data.