Дисертації з теми "Chemical processes Design"

Thesis (PhDEng)--University of Stellenbosch, 2001.
ENGLISH ABSTRACT: Separation processes are an integral part of chemical engineering. The purity of a chemical product is among the principal factors influencing its value. Therefore, any method that can increase the purity of a product or decrease the cost of purification will have a direct effect on the profitability of the entire plant. An important class of separation processes is the solvent-based separations. This includes processes like extractive distillation, liquid-liquid extraction and chromatographic separation. Heterogeneous azeotropic distillation is closely related to these processes. The most important variable in the design of a solvent-based separation process is the choice of solvent. A genetic algorithm for the computer-aided molecular design of solvents for extractive distillation had been previously developed by the author. This algorithm was improved and expanded to include liquid-liquid extraction, heterogeneous azeotropic distillation, gas-liquid chromatography and liquid-liquid (partition) chromatography. At the same time the efficiency of the algorithm was improved, resulting in a speed increase of up to 500% in certain cases. An automatic parameter tuning algorithm was also implemented to ensure maximum efficiency of the underlying genetic algorithm. In order to find suitable entrainers for heterogeneous azeotropic distillation a method is required to locate any ternary heterogeneous azeotropes present in a system. A number of methods proposed in the literature were evaluated and found to be computationally inefficient. Two new methods were therefore developed for ternary systems. A methodology for applying these methods to quaternary and higher systems was also proposed. Two algorithms to design blended solvents were also developed. Blended solvents allow the use of simpler and thus cheaper solvents by spreading the active functional groups over several molecular backbones. It was observed in a number of cases that the blended solvents performed better than their individual components. This was attributed to synergistic interactions between these components. Experimental evidence for this effect was also found. The algorithm was applied to a number of industrially important separation problems, including the extremely difficult final purification process of alpha olefins. In each case solvents were found that are predicted to perform substantially better than those that are currently used in industry. A number of these predictions were tested by experiment and found to hold true.
AFRIKAANSE OPSOMMING: Skeidingsprosesse is 'n integrale deel van chemiese ingenieurswese. Die suiwerheid van 'n chemiese produk is een van die hoof faktore wat die waarde daarvan bepaal. Derhalwe sal enige metode wat die suiwerheid van 'n produk kan verbeter, of die koste van die suiwering daarvan kan verlaag, 'n direkte effek op die winsgewendheid van die hele aanleg hê. 'n Belangrike groep skeidingsprosesse is die oplosmiddel-gebaseerde skeidings. Dit sluit prosesse soos ekstraktiewe distillasie, vloeistofvloeistof ekstraksie en chromatografiese skeidings in. Heterogene azeotrope distillasie is nou verwant aan hierdie prosesse. Die belangrikste veranderlike in die ontwerp van so 'n oplosmiddel-gebaseerde proses is die keuse van oplosmiddel. 'n Genetiese algoritme vir die rekenaargesteunde molekulêe ontwerp van oplosmiddels vir ekstraktiewe distillasie is voorheen ontwikkel deur die skrywer. Hierdie algoritme is verbeter en uitgebrei om vloeistofvloeistofekstraksie, heterogene azeotrope distillasie, gas-vloeistof chromatografie en vloeistof-vloeistof (verdelings) chromatografie in te sluit. Ter selfde tyd is die doeltreffendheid van die algoritme verbeter, wat 'n verbetering in spoed van tot 500% in sekere gevalle tot gevolg gehad het. 'n Algoritme om die parameters van die onderliggende genetiese algoritme outomaties te verfyn is ook geïm plementeer om die optimale werksverrigting van die algoritme te verseker. Om gepaste saamsleepmiddels vir heterogene azeotrope distillasie te vind, word 'n metode benodig om enige ternêre heterogene azeotrope aanwesig in 'n stelsel op te spoor. 'n Aantal sulke metodes wat in die literatuur voorgestel is, is geëvaluEer en daar is gevind dat hierdie metodes ondoeltreffend is. Twee nuwe metodes is derhalwe ontwikkel vir ternêre stelsels. 'n Metodiek om hierdie metodes op kwaternêre en hoër stelsels toe te pas, is ook voorgestel. Twee algoritmes vir die ontwerp van gemengde oplosmiddels is ook ontwikkel. Gemengde oplosmiddels laat die gebruik van eenvoudiger en dus goedkoper oplosmiddels toe, deur die aktiewe funksionele groepe oor 'n aantal molekulêe strukture te versprei. Daar is 'n aantal gevalle waargeneem waar die mengsel beter skeiding bewerkstellig het as die individuele oplosmiddels waaruit dit bestaan. Dit is toegeskryf aan 'n sinergistiese wisselwerking tussen die komponente van die mengsel. Eksperimentele getuienis vir hierdie effek is ook ingewin. Die algoritme is toegepas op 'n aantal belangrike skeidingsprobleme vanuit die bedryf, insluitende die uiters moeilike finale suiwering van alfa olefiene. In elke geval is oplosmiddels gevind wat volgens voorspelling aansienlike beter skeidings sal bewerkstellig as dié wat tans in die bedryf gebruik word. 'n Aantal van hierdie voorspellings is eksperimenteel getoets en korrek bewys.

El desarrollo de procesos químicos eficientes, tanto desde un punto de vista económico como desde un punto de vista ambiental, es uno de los objetivos principales de la Ingeniería Química. Para conseguir este propósito, durante los últimos años, se están empleando herramientas avanzadas para el diseño, simulación, optimización y síntesis de procesos químicos, las cuales permiten obtener procesos más eficientes y con el menor impacto ambiental posible. Uno de los aspectos más importantes a tener en cuenta para diseñar procesos más eficientes es la disminución del consumo energético. El consumo energético del sector industrial a nivel global representa aproximadamente el 22.2 % del consumo energético total, y dentro de este sector, la industria química representa alrededor del 27 %. Por lo tanto, el consumo energético de la industria química a nivel global constituye aproximadamente el 6 % de toda la energía consumida en el mundo. Además, teniendo en cuenta que la mayor parte de la energía consumida es generada principalmente a partir de combustibles fósiles, cualquier mejora de los procesos químicos que reduzca el consumo energético supondrá una reducción del impacto ambiental. El trabajo recopilado en esta Tesis Doctoral se ha llevado a cabo dentro del grupo de investigación COnCEPT, perteneciente al Instituto Universitario de Ingeniería de los Procesos Químicos de la Universidad de Alicante, durante los años 2014 y 2017. El objetivo principal de la presente Tesis Doctoral se centra en el desarrollo de herramientas y modelos de simulación y optimización de procesos químicos con el fin de mejorar la eficiencia energética de éstos, lo que conlleva a la disminución del impacto ambiental de los procesos. Más concretamente, esta Tesis Doctoral se compone de dos estudios principales, que son los objetivos concretos que se pretenden conseguir: - Estudio y evaluación de los modelos surrogados para la mejora en la optimización basada en simuladores de procesos químicos. - Desarrollo de nuevos modelos para la optimización de procesos químicos y la integración de energía simultánea, para redes de intercambiadores de calor.

In recent years, new regulatory guidance has spurred organizations to replace hazardous chemicals with safer alternatives. The factors and influences that shape decisions to transition to safer chemicals are of interest to decision scientists. Previous studies have examined the role that various factors, such as regulation, health impacts, and environmental impacts, have played in shaping such decisions. However, two key research gaps have been identified. First, existing semi-quantitative-based studies do not adequately capture the complexity of decision-making. Second, no in-depth qualitative study of a current substitution process, elucidating decision-making mechanisms at various stages of the design process, has yet been performed. The current research addresses these gaps. The first component of the study is an extensive survey of product and chemical manufacturers to elicit potential tradeoffs concerning final product design and redesign decisions. Such tradeoffs are characterized by a set of six factors affecting product design, which are further disaggregated into thirty-three attributes distributed across these factors. Statistical methods including Bayesian Dirichlet modeling and Principal Component Analysis were used to show: 1) two factors were statistically significantly different than other factors, 2) how features such as company size and time of decision affected factor weighting, and 3) that nine principal components explain 79% of the variance in the attribute scores. The second component of the study was a phenomenological assessment of a current substitution process: replacement of cadmium with Zn-Ni for aircraft components, undertaken by the U.S. Navy and Air Force. This study synthesized existing research in cognition, decision-making, and knowledge management. Semi-structured interviews were conducted with participants representing engineering, environmental, safety, and management disciplines. Qualitative analysis was used to identify and characterize the underlying mechanisms guiding the decision process, including external/internal influences, organizational structure and inertia, and innovative team problem solving. The results from this research contribute to theoretical knowledge in decision-making and cognition, as well as practical knowledge for organizations and policymakers. The broader implications of this research study include a realization that decision tradeoffs vary based on decision contexts, indicating that sector-specific future policy and guidance efforts are needed.

The design of a chemical process is a complex optimization problem. Process models are used to describe the interrelationship of the variables being analyzed and provide the means for representing the description of the problem in mathematical terms. Traditionally, deterministic optimization has been employed to obtain values of design and control variables which minimize capital and operating costs. Subsequently, over-design or safety factors are applied to design variables to account for the fact that plant operating parameters and input variables are uncertain. This design strategy has some limitations. Over-design factors unnecessarily increase capital costs and do not guarantee that the process will have feasible operation. Moreover, the deterministic optimization fails to explore the benefits of integrating operability issues, such as flexibility and robustness, at the design stage. This work examined integrated robust techniques for the design of chemical processes and employed a rigorous stochastic optimization approach that fully take into account the uncertainty of technical parameters and the inherent variability of input variables. In addition to capital and operating costs, quality costs were considered. The influence of uncertain parameters on the final cost was also studied and sensitivity analyses were performed to determine their impact. It was found that the optimal values of the process variables were affected by the uncertainty of the parameters. That is, they presented considerable sensitivity. The location of the optimal values of these variables was also influenced by the components of the overall cost. It is shown that capital and operating costs tend to increase when quality costs contribute in great extent to the overall cost. The overall cost was also influenced by the desired extent of robustness in quality variables. The stochastic optimization formulation turns the objective function into a probabilistic representation, commonly in form of expectations. Contrary to the common approach, where quadrature or cubature formulas are used, this work employed sampling techniques to estimate the expected value of the objective function of the stochastic optimization problem. It was found that, at a modest computational effort, the Hammersley Sequence Sampling (HSS) provides a more accurate estimate of the objective function than cubatures and Monte Carlo sampling techniques. Three quasi-Monte-Carlo sampling techniques, based on the sequences of Halton, Faure and Sobol, were also examined for their potential used in the solution of stochastic optimization problems. These sampling techniques were implemented in a computer code in MATLAB and tested in two engineering problems. It was found that the proposed sampling techniques, Halton Sequence Sampling (HalSS), Faure Sequence Sampling (FSS) and Sobol Sequence Sampling (SSS), can compete with Hammersley Sequence Sampling (HSS) in terms of their low required sample sizes and number of function evaluations to achieve sufficient accuracy.

Resum La societat es cada dia més conscient de la escassetat i els canvis ambientals. Per això les empreses químiques han d’adaptar i desarrollar processos químics més sostenibles. En aquest sentit, hi ha una clara demanda dins de la comunitat científica per desenvolupar eines sistemàtiques per aconseguir reduccions en els costos de producció i en l’impacte ambiental en els processos químics. Aquesta Tesis introdueix un nou mecanisme pel disseny de processos químics més sostenibles. El sistema que es presenta està basat en l’ús combinat de simulació de processos, eines d’optimització multi-objectiu, anàlisis econòmic, anàlisis de cicle de vida i sistemes de suport a la presa de decisions. L’estratègia presentada s’utilitza en la resolució de problemes complexos, pel que també serà necessari desenvolupar nous algoritmes i estratègies de descomposició per dividir el problema original, en sub-problemes més manejables, per obtindre el disseny òptim del procés. La Tesis es presenta utilitzant sis articles que han estat publicats en revistes científiques. La primera part, que inclou dues publicacions, està enfocada en el disseny de bioprocessos sostenibles, ja que aquests processos han guanyat molt interès al mercat degut al seu alt valor afegit. En el primer treball, s’ha estudiat la maximització del Valor Actual Net en la producció de l’amino-acid L-lisina. El cas es formulat com un problema d’optimització dinàmica entera mixta, i es soluciona mitjançant un mètode de descomposició que itera entre els sub-problemes esclau i el mestre. L’optimització dinàmica del problema esclau es resolta mitjançant un algoritme seqüencial que integra el simulador de procés (SuperPro Designer®) amb un solver de problemes de programació no lineal implementat en Matlab®. En el segon article, el problema d’optimització permet una resolució conjunta dels aspectes econòmics i ambientals del procés. En aquest cas s’optimitza el Valor Actual Net conjuntament amb diversos indicadors ambientals. La solución del problema es presentada mitjançant diverses corbes de Pareto, i en elles s’aplica l’anàlisi de components principals per tal de trobar objectius redundants entre els diversos indicadors ambientals. Degut a que la demanda d’energia s’ha incrementat dràsticament durant els últims anys, l’anàlisi energètic en els processos industrial ha guanyat molt interès. Es per això, que la segona part de la Tesis està enfocada en el disseny òptim de cicles termodinàmics. En aquesta secció s’han publicat dos articles. En el primer dels articles d’aquesta segona part es presenta un mètode per el disseny òptim de cicles d’absorció d’amoni-aigua, tenint en compte l’anàlisi econòmic i ambiental. El problema es planteja com un problema de programació no-lineal entera amb multiples objectius i es resol amb l’estratègia d’aproximació exterior. Al segon article, s’aplica una estratègia similar però incloent diversos cicles termodinàmics. En aquest article es demostren les capacitats del mètode amb diversos cicles termodinàmics, com un cicle Rankine de 10 MW modelat en Aspen Hysys® i un cicle d’absorció d’ammonia-aigua de 90 kW modelat en Aspen Plus®. La producció de biocombustibles segueix creixent a nivell mundial a una gran velocitat. A la tercera part d’aquesta Tesis, hem aplicat tècniques matemàtiques per desenvolupar processos de producció de biocombustibles. Aquesta tercera part inclou novament dos publicacions. En la primera s’adreça el problema de reduir l’impacte ambiental de les plantes de producció de biodiesel mitjançant la inclusió de panells solars per la generació del vapor utilitzat en la planta. Per dur a terme l’estudi s’utilitza un model de sistemes d’energia solar que inclou emmagatzematge d’energia implementat en GAMS®. Aquest model es combinat amb un model de simulació rigorós desenvolupat amb Aspen Plus® de la planta de biodiesel. En el problema el sistema d’energia solar té en compte la minimització del cost i del potencial d'escalfament global de la planta. En el segon treball, el problema adreça el disseny multi-objectiu d’una planta de producció de bio etanol combinada amb un sistema de panels solars per la generació de vapor en la planta. Globalment es pot considerar que la Tesis presenta un nou marc en l’àmbit del disseny òptim de processos sostenibles. Els resultants numèrics mostren que es possible aconseguir millores ambientals i econòmiques utilitzant aquest enfoc rigorós. Addicionalment, aquest mètode ha estat aplicat a diferents tipus de processos com: bioprocesos, cicles termodinàmics i biocombustibles. Aquest mètode serà molt útil per els prenedors de decisions a fi d'avaluar la topologia i les condicions de funcionament en l'enginyeria de procés.
Resumen La sociedad es cada día más consciente de la escasez y los cambios ambientales. Por lo que las empresas químicas tienen la necesidad de adaptarse y desarrollar procesos químicos más sostenibles. Por lo tanto, se ha creado una clara demanda dentro de la comunidad científica para desarrollar herramientas sistemáticas que consigan reducciones en el coste y en el impacto ambiental de los procesos químicos. Esta Tesis introduce un nuevo mecanismo para el diseño de procesos químicos más sostenibles. El sistema que se presenta está basado en el uso combinado de simulación de procesos, herramientas de optimización multi-objetivo, análisis económico, análisis de ciclo de vida y sistemas de soporte a la toma de decisiones. La estrategia presentada se utiliza en la resolución de problemas complejos, por lo que será también necesario desarrollar nuevos algoritmos y estrategias de descomposición para dividir el problema original, en sub-problemas más manejables, para obtener el diseño óptimo del proceso. La Tesis se presenta utilizando seis artículos que se han publicado en revistas científicas. La primera parte, que incluye dos publicaciones, está enfocada en el diseño de bioprocessos sostenibles, ya que han ganado mucho interés en el mercado debido a su alto valor. En el primer trabajo, se ha estudiado la maximización del Valor Actual Neto en la producción del amino-ácido L-lisina. El problema se formula como un problema de optimización dinámica entera mixta, solucionándolo mediante un método de descomposición que itera entre los sub-problemas esclavo y maestro. La optimización dinámica del problema esclavo es resuelta mediante un algoritmo secuencial que integra el simulador de proceso (SuperPro Designer®) con un solver de problemas de programación no lineal implementado en Matlab®. En el segundo artículo, el problema de optimización permite una resolución conjunta de los aspectos económicos y ambientales del proceso. En este caso se optimiza el Valor Actual Neto conjuntamente con diferentes indicadores ambientales. La solución del problema se presenta mediante diferentes curvas de Pareto, a las que se aplica el análisis de componentes principales. Debido a que la demanda de energía a incrementado drásticamente durante los últimos años, el análisis energético en los procesos industriales ha ganado mucho interés. Es por eso que en la segunda parte de la Tesis nos enfocamos en el diseño óptimo de ciclos termodinámicos. En esta sección se han publicado dos artículos. En el primero de esta segunda parte se presenta un método para el diseño óptimo de ciclos de absorción de amoníaco-agua, teniendo en cuenta el análisis económico y ambiental. El caso se plantea como un problema de programación no-lineal entera con múltiples objetivos. En el segundo, se aplica una estrategia similar al primero pero a diversos ciclos termodinámicos. En este trabajo se demuestran las capacidades del método con diversos ciclos termodinámicos. Entre ellos un ciclo Rankine de 10 MW simulado en Aspen Hysys® y un ciclo de absorción de amoníaco-agua de 90 kW simulado en Aspen Plus®. La producción de biocombustibles sigue creciendo a nivel mundial a una gran velocidad. En la tercera parte de la Tesis, hemos aplicado herramientas matemáticas a desarrollar procesos de producción de biocombustibles. Esta tercera parte incluye nuevamente dos publicaciones. En el primer trabajo el problema es reducir el impacto ambiental de las plantas de producción de biodiesel mediante la inclusión de paneles solares para la generación del vapor utilizado en dicha planta. Para realizar este estudio se utiliza un modelo de sistemas de energía solar en GAMS®. Este modelo se combina con un modelo de simulación riguroso en Aspen Plus® de la planta de biodiesel. En el problema el sistema de energía solar tiene en cuenta la minimización del coste y del potencial de calentamiento global. En el segundo trabajo, el problema es el diseño multi-objectivo de una planta de producción de bioetanol combinada con un sistema de paneles solares para la generación de vapor en la planta. En general, se puede considerar que la Tesis presenta un marco interesante en el ámbito del diseño optimo de procesos sostenibles. Los resultados numéricos muestran cómo es posible conseguir mejoras ambientales y económicas utilizando estos modelos rigurosos. Además, este método ha sido aplicado a diferentes tipos de procesos como: los bioprocesos, los ciclos termodinámicos y los biocombustibles. Este método será muy útil para los tomadores de decisiones a fin de evaluar la topología y las condiciones de funcionamiento en la ingeniería de proceso.
The society is every day more conscious about the scarce of resources, the global economy, and the environmental changes. Hence, chemical companies have the necessity to be adapted and develop more sustainable processes. There is a clear demanding to the scientific community to develop systematic tools to achieve reductions in the production costs as well as the associated environmental impact in order to develop decision support tools for the design of chemical plants. This thesis introduces a novel framework for the optimal design of sustainable chemical processes. Our approach combines process simulation, multi-objective optimization tools (MOO), economic analysis, life cycle assessment (LCA) and decision support systems (DSS). The developed strategy will be used to solve very complex problems. For that it will be necessary to develop new algorithms and decomposition strategies to divide the original problem in more manageable sub-problems, to obtain the optimum design of the process. The capabilities of the methodology have been tested in different processes along the Ph.D Thesis. This PhD dissertation is presented using six articles that have been published in international peer reviewed journals. The first part, which includes two publications, is focused in the development of sustainable bioprocesses, as these processes have recently gained wider interest for their potential to produce high-value products. In the first work, we studied the maximization of the Net Present Value (NPV) in the production of the amino acid L-lysine. The design task is mathematically formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a decomposition method that iterates between primal and master sub-problems. The dynamic optimization primal sub-problems are solved via a sequential approach that integrates the process simulator SuperPro Designer® with an external non-linear programming (NLP) solver implemented in Matlab®, while the task of the master problem is to decide on the value of the integer variables. In the second paper, the optimization allows for the simultaneous consideration of economic and environmental concerns. We optimize in this case the economic (NPV) and different environmental indicators. The solution is given by various bi-objective Pareto sets, and then we applied principal component analysis (PCA) in order to find redundant objective functions between the environmental indicators. Because the energy demand has drastically increased over the last few years, the energetic analysis of industrial processes has gained wider interest. Hence, we focused in the second part of the thesis in the optimal design of thermodynamic cycles. In this section, we published two papers. In the first article of the second part we present a method for the optimal design of ammonia-water absorption cycles for cooling and refrigeration applications with economic and environmental concerns. The design task is posed as multi-objective mixed-integer non-linear programming (MINLP). In the second article, we expand our work to different thermodynamic cycles. We demonstrate the capabilities of the approach with a 10 MW Rankine cycle simulated in Aspen Hysys® and a 90 kW ammonia-water absorption cycle in Aspen Plus®. Biofuels production worldwide is continuing to grow at very rapid pace. Hence, in the third part of the thesis, we applied the techniques developed in different biofuels production processes. This third part includes two publications. In the first work we address the problem of reducing the environmental impact of biodiesel plants through their integration with a solar thermal energy system that generates steam. A mathematical model of the solar energy system that includes energy storage is programmed and coupled with a rigorous simulation model of the biodiesel facility developed in Aspen Plus®. The solar energy system accounts for the simultaneous minimization of cost and global warming potential. In the second work, we address MOO of a corn-based bioethanol plant coupled with solar assisted steam generation system with heat storage. Our approach relies on the combined use of process simulation, rigorous optimization tools and, economic and energetic plant analysis. Overall, we can consider that this thesis presents a promising framework for the optimal design of sustainable chemical processes. Numerical results show that it is possible to achieve environmental and cost saving using this rigorous approach. Additionally, this approach has been applied in very different type of processes, such as: bioprocesses, thermodynamic cycles and biofuels. This methodology will be very useful for decision-makers in order to evaluate the topology and operating conditions in process system engineering

This thesis focuses on studying and investigation the effects of the hydrodynamic and operating parameters in the air-biomass gasification in a bubbling fluidised bed gasifier under low temperature (< 800°C) conditions and evaluating the potential of the gasification of two solid biomass waste materials, Iraqi date palm wastes and sawdust pinewood. These parameters are air flowrate, particle size of the sand bed material, biomass particle size, static bed height, air equivalence ratio, bed temperature, number of holes in distributor plates and biomass fuel type. A design study was conducted to provide preliminary data for designing and constructing a large lab-scale fluidised bed column, diameter D=8.3cm, for cold and hot fluidisation experiments. Cold fluidisation experiments were conducted to provide the fluidisation behaviour data for the sand, biomass and their mixture. The design and cold fluidisation results have shown a compatible finding in the following: 1) the design parameter Umf has shown that, it increases as sand particle size increases. 2) It was not affected by static bed height. In addition, cold fluidisation results show that: sand has a high fluidisation quality compared to pure biomass and sand-biomass mixture and there is no effect of the bed static height on the Umf. In general, the studied parameters on the air-biomass gasification performance have shown that: 1) air flowrate has a considerable effect, 2) as sand and biomass particle size increases a weakened gasification was achieved. 3) The static height effect has been observed due to the location of the biomass feeding position thereby affecting reactant residence time. 4) For equivalence ratio range (0.2-0.4) the lowest value provided optimum gas composition and LHV values, whereas the highest value ER = 0.43 provided highest (CCE), CGE) and (GY). 5) No significant effect was seen for bed temperature between 360 to 465oC. 6) A considerable effect has been shown for the distributor plate configuration. Finally, the results has shown that SPWB has potential compared to IDPWB for energy generation. However, additional simulation, optimization and experimental studies on the bubbling fluidised biomass gasification for a broad range of operating and hydrodynamic parameters are hereby suggested.

This research project offers new knowledge in the field of artificial patination and extends the application of colour to metals that have previously not been exploited for their colouring properties, specifically pewter and its main constituent, tin. The investigation defines, refines and applies new and evolved processes and techniques that provide novel opportunities for both craft and industry through the development of new products, particularly jewellery and tableware items. The thesis details the actions taken to substantiate the hypothesis that pewter and tin may be coloured by chemical means. This includes the interpretation, exploitation and creative application of developed processes to the design and production of new works. It also discusses the research method applied and the rationales that have informed decision making. The research project is reviewed in separate chapters discussing each phase of the investigation. These phases are:  The selection, testing and analysis of chemical colouring processes within the workshop and laboratory.  Investigation into working properties of the coloured material and surface pattern development.  The creative application of outcomes from the above two phases to new works.  Evaluation of the research outcomes in relation to creative practice and their potential for wider commercial application The chemical exploration and design development phases of the research have been conducted in association with the International Tin Research Institute (ITRI Ltd) and the manufacturing industry, which has confirmed the validity of results. The model of collaboration adopted and associated outcomes are also discussed. Finally, the thesis considers the impact of the research outcomes on the material, creative practice and implications for further investigation including the potential for wider commercial application of the patination procedures developed.

In the chemical or biochemical industry most processes are modeled by nonlinear equations. It is of a great significance to design high-performance nonlinear controllers for efficient control of these nonlinear processes to achieve closed-loop system's stability and high performance. However, there are many difficulties which hinder the design of such controllers due mainly to the process nonlinearity. In this work, comprehensive design procedures based on robust control have been proposed to efficiently deal with the design of gain-scheduled controllers for nonlinear systems. Since all the design procedures proposed in this work rely strongly on the process model, the first difficulty addressed in this thesis is the identification of a relatively simple model of the nonlinear processes under study. The nonlinearity of the processes makes it often difficult to obtain a first-principles model which can be used for analysis and design of the controller. As a result, relatively simple empirical models, Volterra series model and state-affine model, are chosen in this work to represent the nonlinear process for the design of controllers. The second major difficulty is that although the nonlinear models used in this thesis are easy to identify, the analysis of stability and performance for such models using nonlinear control theory is not straightforward. Instead, it is proposed in this study to investigate the stability and performance using a robust control approach. In this approach, the nonlinear model is approximated by a nominal linear model combined with a mathematical description of model error to be referred to, in this work, as model uncertainty. In the current work it was assumed that the main source of uncertainty with respect to the nominal linear model is due to the system nonlinearity. Then, in this study, robust control theoretical tools have been especially developed and applied for the design of gain-scheduled Proportional-Integral (PI) control and gain-scheduled Model Predictive Control (MPC). Gain-scheduled controllers are chosen because for nonlinear processes operated over a wide range of operation, gain-scheduling has proven to be a successful control design technique (Bequette, 1997) for nonlinear processes. To guarantee the closed-loop system's robust stability and performance with the designed controllers, a systematic approach has been proposed for the design of robust gain-scheduled controllers for nonlinear processes. The design procedure is based on robust stability and performance conditions proposed in this work. For time-varying uncertain parameters, robust stability and performance conditions using fixed Lyapunov functions and parameter-dependent Lyapunov functions, were used. Then, comprehensive procedures for the design and optimization of robust gain-scheduled PI and MPC controllers tuning parameters based on the robust stability and performance tests are then proposed. Since the closed-loop system represented by the combination of a state-affine process model and the gain-scheduled controller is found to have an affine dependence on the uncertain parameters, robust stability and performance conditions can be tested by a finite number of Linear Matrix Inequalities (LMIs). Thus, the final problems are numerically solvable. One of the inherent problems with robust control is that the design is conservative. Two approaches have been proposed in this work to reduce the conservatism. The first one is based on parameter-dependent Lyapunov functions, and it is applied when the rate of change of the time-varying uncertainty parameters is a priori available. The second one is based on the relaxation of an input-saturation factor defined in the thesis to deal with the issue of actuator saturation. Finally, to illustrate the techniques discussed in the thesis, robust gain-scheduled PI and MPC controllers are designed for a continuous stirred tank reactor (CSTR) process. A simple MIMO example with two inputs and two outputs controlled by a multivariable gain-scheduled MPC controller is also discussed to illustrate the applicability of the methods to multivariable situations. All the designed controllers are simulated and the simulations show that the proposed design procedures are efficient in designing and comparing robust gain-scheduled controllers for nonlinear processes.

Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xvi, 190 p.; also includes graphics (some col.). Includes bibliographical references (p. 182-190). Available online via OhioLINK's ETD Center

In recent years, the development and application of multi-objective optimization techniques have received increasing attention in the literature. Recent innovations in the fields of systems science, artificial intelligence, and operations research have led to the development of rigorous multi-objective optimization techniques to address the problem of optimizing complex processes in the presence of multiple conflicting objectives. This thesis is a collection of three papers that focuses on the development and application of a multi-objective optimization strategy for selecting optimum operating conditions for the production of gluconic acid and for determining optimum tuning parameters for PID controllers. The optimization strategy developed is performed in two steps: the approximation of the set of feasible solutions called the Pareto domain using the Dual Population Evolutionary Algorithm and the classification of the domain using the Net Flow Method, which incorporates information on the process provided by an expert. This strategy has been proven to be robust in determining the optimal solution after studying twelve standard test cases, which have been used frequently in the literature, and two engineering problems. In addition, the Pareto domain per se provides very useful information on the quality of the optimal zone.

This thesis presents a formal method for the the design of optimal and provably correct procedural controllers for chemical processes modelled as Stochastic Discrete Event Systems (SDESs). The thesis extends previous work on Procedural Control Theory (PCT) [1], which used formal techniques for the design of automation Discrete Event Systems (DESs). Many dynamic processes for example, batch operations and the start-up and shut down of continuous plants, can be modelled as DESs. Controllers for these systems are typically of the sequential type. Most prior work on characterizing the behaviour of DESs has been restricted to deterministic systems. However, DESs consisting of concurrent interacting processes present a broad spectrum of uncertainty such as uncertainty in the occurrence of events. The formalism of weighted probabilistic Finite State Machine (wp-FSM) is introduced for modelling SDESs and pre-de ned failure models are embedded in wp-FSM to describe and control the abnormal behaviour of systems. The thesis presents e cient algorithms and procedures for synthesising optimal procedural controllers for such SDESs. The synthesised optimal controllers for such stochastic systems will take into consideration probabilities of events occurrence, operation costs and failure costs of events in making optimal choices in the design of control sequences. The controllers will force the system from an initial state to one or more goal states with an optimal expected cost and when feasible drive the system from any state reached after a failure to goal states. On the practical side, recognising the importance of the needs of the target end user, the design of a suitable software implementation is completed. The potential of both the approach and the supporting software are demonstrated by two industry case studies. Furthermore, the simulation environment gPROMS was used to test whether the operating speci cations thus designed were met in a combined discrete/continuous environment.

The main result of underground mining extraction is creating of large underground voids (mine stopes). These empty openings are typically backfilled with an engineering cementitious material called cemented paste backfill (CPB). The main purpose of CPB application in underground mining is to provide stability and ensure the safety of underground openings, maximize ore recovery, and also provide an environmental-friendly means of underground disposal of potential acid generating tailings. CPB is a mixture of mine tailings, cement binder and water. CPB has a complex geotechnical behaviour when poured into mine voids. This is because of the different thermal (T), hydraulic (H), mechanical (M) and chemical coupled processes and interactions that take place in CPB soon after placement. In addition to these THMC behaviours, various external factors, such as stope geometry, drainage condition and arching effects add more complexity to its behaviour. In order to acquire a full understanding of CPB behaviour, there is a need to consider all of these THMC factors and processes together. So far, there has not been any study that addresses this research need. Indeed, fundamental knowledge of the THMC behaviour of CPB provides a key means for designing safe and cost-effective backfill structures, as well as optimizing mining cycles and productivity of mines. Innovative experimental tools and CPB testing methods have been developed and adopted in this research to fulfill the objectives of this research. In the first phase of the study, experiments with high columns are developed to study the THMC behaviour of CPB from early to advanced ages with respect to height of the column and curing time. The column experiments simulate the mine stope and filling sequence and provide an opportunity to study external factors, such as evaporation, on the THMC behaviour of CPB. However, an important factor is the overburden pressure from the stress due to self-weight that cannot be simulated through column experiments. Therefore, in the second phase of this study, a novel THMC curing under stress apparatus is developed to study the THMC behaviour of CPB under various pressures due to the self-weight of the CPB, drainage conditions, and filling rate and sequence. Comprehensive instrumentation and geotechnical testing are carried out to obtain fundamental knowledge on the THMC behaviour of CPB in different curing conditions from early to advanced ages. The results of these studies show that the THMC properties of CPB are coupled. Important parameters, such as curing stress, self-desiccation due to cement hydration, temperature, pore water chemistry, and mineralogical and chemical properties of the tailings, have significant influence on the shear strength and compressive strength development of CPB. Factors such as evaporation and drying iii shrinkage can also affect the hydro-mechanical properties of CPB. The curing conditions (such as curing stress, drainage and filling rate) also has significant impact on CPB behaviour and performance. The THMC interactions and the degree of influence of each factor should be included in designing backfill structures and planning mining cycles. This innovative curing under stress technique can be replaced the conventional curing of CPB (curing under zero stress and no THMC loadings), in order to optimize CPB mechanical strength assessment, increase mine safety and enhance the productivity.

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The present study demonstrates the application of decision tree algorithms to the co-crystallization process. Fifty four (54) batches of carbamazepine–salicylic acid co-crystals embedded in poly(ethylene oxide) were manufactured via hot melt extrusion and characterized by powder X-ray diffraction, differnetial scanning calorimetry, and near-infrared spectroscopy. This dataset was then applied in WEKA, which is an open-sourced machine learning software to study the effect of processing temperature, screw speed, screw configuration, and poly(ethylene oxide) concentration on the percentage of co-crystal conversion. The decision trees obtained provided statistically meaningful and easy-to-interpret rules, demonstrating the potential to use the method to make rational decisions during the development of co-crystallization processes.

Sizing (hydrophobization) is one of the most important process steps within the added-value chain of about 1/3rd of the worldwide produced paper & board products. Even though sizing with so-called reactive sizing agents, such as alkenyl succinic anhydride (ASA) was implemented in the paper industry decades ago, there is no total clarity yet about the detailed chemical and physical mechanisms that lead to their performance. Previous research was carried out on the role of different factors influencing the sizing performance, such as bonding between ASA and cellulose, ASA hydrolysis, size revision as well as the most important interactions with stock components, process parameters and additives during the paper making process. However, it was not yet possible to develop a holistic model for the explanation of the sizing performance given in real life application. This thesis describes a novel physico-chemical approach to this problem by including results from previous research and combining these with a wide field of own basic research and a newly developed method that allows tracing back the actual localization of ASA within the sheet structure. The carried out measurements and trial sets for the basic field of research served to evaluate the stock and process parameters that most dominantly influence the sizing performance of ASA. Interactions with additives other than retention aids were not taken into account. The results show that parameters, such as the content of secondary fibers, the degree of refining, the water hardness as well as the suspension conductivity, are of highest significance. The sample sets of the trials with the major impacting parameters were additionally analyzed by a newly developed localization method in order to better understand the main influencing factors. This method is based on optical localization of ASA within the sheet structure by confocal white light microscopy. In order to fulfill the requirements at magnification rates of factor 100 optical zoom, it was necessary to improve the contrast between ASA and cellulose. Therefore, ASA was pretreated with an inert red diazo dye, which does not have any impact on neither the sizing nor the handling properties of ASA. Laboratory hand sheets that were sized with dyed ASA, were analyzed by means of their sizing performance in correlation to measurable ASA agglomerations in the sheet structure. The sizing performance was measured by ultrasonic penetration analysis. The agglomeration behavior of ASA was analyzed automatically by multiple random imaging of a sample area of approx. 8650 µm² with a minimum resolution for particles of 500 nm in size. The gained results were interpreted by full factorial design of experiments (DOE). The trials were carried out with ASA dosages between 0% and 0.8% on laboratory hand sheets, made of 80% bleached eucalyptus short fiber kraft pulp and 20% northern bleached softwood kraft pulp, beaten to SR° 30, produced with a RDA sheet former at a base weight of 100 g/m² oven dry. The results show that there is a defined correlation between the ASA dosage, the sizing performance and the number and area of ASA agglomerates to be found in the sheet structure. It was also possible to show that the agglomeration behavior is highly influenced by external factors like furnish composition and process parameters. This enables a new approach to the explanation of sizing performance, by making it possible to not only examine the performance of the sizing agent, but to closely look at the predominant position where it is located in the sheet structure. These results lead to the explanation that the phenomenon of sizing is by far not a pure chemical process but rather a more physical one. Based on the gained findings it was possible so far to optimize the ASA sizing process in industrial-scale by means of ~ 50% less ASA consumption at a steady degree of sizing and improved physical sheet properties.

This thesis is concerned with methodologies for the accurate quantitative modelling of molecular biological systems. The first part is devoted to the chemical Langevin equation (CLE), a stochastic differential equation driven by a multidimensional Wiener process. The CLE is an approximation to the standard discrete Markov jump process model of chemical reaction kinetics. It is valid in the regime where molecular populations are abundant enough to assume their concentrations change continuously, but stochastic fluctuations still play a major role. We observe that the CLE is not a single equation, but a family of equations with shared finite-dimensional distributions. On the theoretical side, we prove that as many Wiener processes are sufficient to formulate the CLE as there are independent variables in the equation, which is just the rank of the stoichiometric matrix. On the practical side, we show that in the case where there are m_1 pairs of reversible reactions and m_2 irreversible reactions, there is another, simple formulation of the CLE with only m_1+m_2 Wiener processes, whereas the standard approach uses 2m_1+m_2. Considerable computational savings are achieved with this latter formulation. A flaw of the CLE model is identified: trajectories may leave the nonnegative orthant with positive probability. The second part addresses the challenge when alternative, structurally different ordinary differential equation models of similar complexity fit the available experimental data equally well. We review optimal experiment design methods for choosing the initial state and structural changes on the biological system to maximally discriminate between the outputs of rival models in terms of L_2-distance. We determine the optimal stimulus (input) profile for externally excitable systems. The numerical implementation relies on sum of squares decompositions and is demonstrated on two rival models of signal processing in starving Dictyostelium amoebae. Such experiments accelerate the perfection of our understanding of biochemical mechanisms.

Alkoholhaltiga drycker har konsumerats länge i Sverige, idag är öl en av de mest förekommande dryckerna. Med tanke på hur få ingredienser öl innehåller och att smaken trots detta kan varieras i det oändliga är det lätt att förstå att alla delsteg måste genomföras med precision. De olika delstegen i ölbryggningsprocessen är mältning, mäskning, lakning, humlekokning och jäsning. Ett försök till att bygga ett automatiserat bryggverk har gjorts. Komponenter till bryggverket har köpts in och bryggverket har byggts från grunden. Det var inte möjligt att göra alla delsteg i processen automatiserade, till exempel flödesstyrningar och tillsats av ingredienser. Detta gjorde att manuella insatser krävdes. För att styra bryggningen programmerades mikrokontrollerkortet Arduino Uno i programmeringsspråket C. Med hjälp av programmeringen har de delsteg av bryggningen, som går att automatisera, automatiserats. Vid testbryggningen uppstod praktiska problem som inte kunde förutsägas i teorin, exempelvis gick pumpen torrt ibland, reläerna fungerade inte som tänkt samt att en oväntad temperaturgradient uppstod. Dessa problem kan eventuellt lösas med ytterligare insatser i framtida försök. Vidare genomfördes tester innan bryggningen med endast vatten i kärlen för att undersöka temperaturregleringen. Det visade sig dock vara stora skillnader mellan vatten och mäsk, vilket gjorde att dessa tester inte kunde förutsäga alla problem. Trots detta resulterade bryggningen i en besk men drickbar öl.

Energy consumption is one of the main areas in the study of chemical process design. It is usually referred to as the critical element that is continuously needed for running a chemical process, and is daily effected by the prices of energy. Therefore, poor designs which are not energy integrated normally lead to less profit due to high consumption of energy. These simple economics are the reason for tackling the area of energy integration in process design. A styrene production process is taken to be the model process for carrying out the design work incorporating the various energy integration techniques. A thorough review of the published work in this subject area was the first step in this research work. This has been followed by calculating mass and energy balances around the overall plant and the individual process steps, so that information about flowrates and energy consumed and released was obtained for the base case. After this all the possible distillation sequence configurations were tested in order to find the sequence that required least energy compared with all the other possible sequences. This step is the first part of integrating the distillation train. The second part considered the heat exchanger network associated with the distillation train and this has been taken in the context of overall process integration. "Pinch technology" was used as an aid for targeting the minimum hot and cold utilities required, designing the heat exchanger network that was compatible with the minimum use of utility and to seek further improvements on the process heat exchanger network which made it capable of recovering even more energy. Utility supplies are designed with respect to the process design, hence the next step considered the interaction between the utility and process design. Thus, the utilities were introduced in a more efficient way, resulting in a better heat exchanger network and increasing the interprocess heat exchange. Finally the steam and power system in the styrene plant was tested in order to determine how much this system had benefited due to the overall efficiency of energy supply and demand.

Process development of new complex reactions in the pharmaceutical and fine chemicals industries is challenging, and expensive. The field is beginning to see a bridging between fundamental first-principles investigations, and utilisation of data-driven statistical methods, such as machine learning. Nonetheless, process development and optimisation in these industries is mostly driven by trial-and-error, and experience. Approaches that move beyond these are limited to the well-developed optimisation of continuous variables, and often do not yield physical insights. This thesis describes several new methods developed to address research questions related to this challenge. First, we investigated whether utilising physical knowledge could aid statistics-guided self-optimisation of a C-H activation reaction, in which the optimisation variables were continuous. We then considered algorithmic treatment of the more challenging discrete variables, focussing on solvents. We parametrised a library of 459 solvents with physically meaningful molecular descriptors. Our case study was a homogeneous Rh-catalysed asymmetric hydrogenation to produce a chiral γ-lactam, with conversion and diastereoselectivity as objectives. We adapted a state-of-the-art multi-objective machine learning algorithm, based on Gaussian processes, to utilise the descriptors as inputs, and to create a surrogate model for each objective. The aim of the algorithm was to determine a set of Pareto solutions with a minimum experimental budget, whilst simultaneously addressing model uncertainty. We found that descriptors are a valuable tool for Design of Experiments, and can produce predictive and interpretable surrogate models. Subsequently, a physical investigation of this reaction led to the discovery of an efficient catalyst-ligand system, which we studied by operando NMR, and identified a parametrised kinetic model. Turning the focus then to ligands for asymmetric hydrogenation, we calculated versatile empirical descriptors based on the similarity of atomic environments, for 102 chiral ligands, to predict diastereoselectivity. Whilst the model fit was good, it failed to accurately predict the performance of an unseen ligand family, due to analogue bias. Physical knowledge has then guided the selection of symmetrised physico-chemical descriptors. This produced more accurate predictive models for diastereoselectivity, including for an unseen ligand family. The contribution of this thesis is a development of novel and effective workflows and methodologies for process development. These open the door for process chemists to save time and resources, freeing them up from routine work, to focus instead on creatively designing new chemistry for future real-world applications.