Process systems engineering has provided with a range of powerful tools to chemical engineers for synthesis, optimisation and control using thorough understanding of the processes enhanced with the aid of sophisticated and accurate multi-faceted mathematical models. Crystallization processes have rarely benefited from these new techniques, for they lack in models that could be used to bridge the gaps in their perception before utilising the resulting insight for the three above mentioned tasks. In the present work, first a consistent and sufficiently complex models for unit operations including MSMPR crystallizer, hydrocyclone and fines dissolver are developed to enhance the understanding of systems comprising these units. This insight is then utilised for devising innovative techniques to synthesise, optimise and control such processes. A constructive targeting approach is developed for innovative synthesis of stage-wise crystallization processes. The resulting solution surpasses the performance obtained from conventional design procedure not only because optimal temperature profiles are used along the crystallizers but also the distribution of feed and product removal is optimally determined through non-linear programming. The revised Machine Learning methodology presented here for continual process improvement by analysing process data and representing the findings as zone of best average performance, has directly utilised the models to generate the data in the absence of real plant data. The methodology which is demonstrated through KNO₃ crystallization process flowsheet quickly identifies three opportunities each representing an increase of 12% on nominal operation. An optimal multi-variable controller has been designed for a one litre continuous recycle crystallizer to indirectly control total number and average size of crystals from secondary process measurements. The system identification is solely based on experimental findings. Linear Quadratic Gaussian method based design procedure is developed to design the controller which not only shows excellent set-point tracking capabilities but also effectively rejects disturbance in the simulated closed loop runs.

The accumulation of plastic waste in the natural environment has been a major environmental concern for many decades. However, the environmental impacts associated with leakage are not taken into consideration under current life-cycle based approaches, despite packaging being a major application area of life cycle assessment. Furthermore, there is limited quantitative information on the leakage propensities and rates of different products. This presents a critical limitation during the life cycle management (LCM) of products destined for regions where they are likely to be dumped or littered. This thesis investigates the feasibility and influence of using product specific leakage rates as a proxy indicator for potential marine environmental impacts, to inform the life cycle management of products in which plastic is a material choice. In particular, it explores whether a realistic understanding of leakage rates, differentiated by major use, may facilitate the development of effective interventions to mitigate the growing problem of marine plastic pollution. This entails the quantification of leakage rates for selected plastic items identified as highly prone to leakage based on a series of beach surveys. The potential influence of providing such specific knowledge is investigated via the exploration of current LCM practices for plastic products employed by key value-chain actors in the plastics industry. In addition, the life cycle management of three key items identified as problematic (straws, cotton bud sticks and beverage bottle lids) is explored via a case study approach. Beach accumulation surveys are often used to estimate plastic flows into the marine environment. Thus, two series of beach surveys were conducted across five beaches with varying catchment area characteristics in Cape Town, over two periods in 2017 and 2018 – 2019 respectively. Daily accumulation rates varied across all sites ranging from 38 – 2962 items.day-1 .100m-1 during the first sampling period and 305 – 2082 items.day-1 .100m-1 during the second. Plastic was the major contributor accounting for 85.6 – 98.9% of all items by count. Despite the variations in litter accumulation rates and composition, there was significant commonality in the items which were identified as major contributors. The top 12 most prevalent and abundant identifiable plastic items accounted for 43 – 66% during the first sampling period, and 41 – 73% during the second. Ten of these items were prevalent during both periods, eight of which were associated with food consumed on-the-go, including beverage bottle lids, polystyrene food containers, single sweet wrappers, snack packets and straws. This indicates that the high litterability of these items was consistent across catchment areas and sampling periods. Furthermore, when ratioed to waste generation, items found to be major contributors were found to have significantly higher leakage rates in comparison to less prevalent items. The increasing concern surrounding plastic pollution has pressured value-chain actors to review their approaches to the life cycle management of plastic products. This has led to the development of strategies focussed on plastic packaging which were not commonplace across all companies. However, these strategies are not necessarily aimed at mitigating plastic pollution but are more broadly concerned with sustainable product design, emphasising design for recycling and supporting recycling activities at end-of-life as part of their extended producer responsibility. Thus, the extent to which these strategies address plastic pollution is limited. Furthermore, value-chain actors reported varied approaches to product prioritisation for intervention which are often not grounded in empirical evidence but instead based on anecdotes and limited logic. This may be attributed to a lack of reliable product-specific information surrounding plastic pollution. Such approaches have the potential to prioritise products ii which are not major contributors to marine pollution in lieu of those that are. Interventions targeted towards products that were identified as prone to leakage, including straws and cotton bud sticks, were catalysed by consumer pressure and societal expectations at large. Ultimately, this thesis demonstrates the need for product-specific knowledge on leakage to facilitate responsible and effective life cycle management of products involving plastic as a material choice. Furthermore, it has demonstrated the feasibility of providing such information through the use of leakage rates. Leakage rates have the potential to play an important role in product life cycle management, allowing for the identification of products which are highly prone to leakage into the environment. Thus, their integration into LCM practice has the potential to facilitate the development of targeted strategies to address plastic pollution.

<div>Currently, fossil resources dominate fuel and chemical production landscape. Besides concerns related to the ever-increasing greenhouse gas emission, fossil resources are also limited. In a petroleum-deprived future, sustainably available biomass can serve as a renewable carbon source. Due to its limited availability, however, this biomass resource must be utilized and converted effciently to minimize carbon losses to undesirable by-products. A modeling and optimization approach that can identify optimal process congurations for chemical and fuel production from biomass using stoichiometric and thermodynamic knowledge of the underlying biomass reaction system is proposed in this dissertation. Several case studies were performed with this approach, and the outcomes found agreement with reported experimental results. In particular, a case study on fast-hydropyrolysis vapor of cellulose led to the discovery of new reaction route and provided insights in comprehending the formation of experimentally observed molecules. The modeling and optimization approach consists of two main steps. The rst step is the generation of the search space and the second step is the identication of all optimal reaction routes.</div><div><br></div><div><div>For the rst step, literature review and automated reaction network generator are employed to identify all possible processes for biomass conversion. Through literature review, yield data on processes that generate biomass-derived molecules are collected. As these biomass-derived molecules often possess multiple functional groups, utilization of automated reaction network generator, which considers a set of biomass-derived molecules and reaction rules, enables generation of all possible reactions. In this work, an automated reaction network generator tool called Rule Input Network Generator is utilized. Using this generated search space, a mathematical optimization problem, which identies the optimal reaction network, is constructed. For the second step, the optimization problem identies all reaction routes with the minimum number of reactions for a given set of biomass and target products. This formulation constructs a process superstructure that contains processes that generate biomass-derived molecules and all possible reactions from biomass-derived molecules. In this optimization problem, the main constraint for the reaction is its thermodynamic favorability within a certain temperature range. Using optimization solver, optimal solutions for this problem are obtained.</div></div><div><br></div><div><div>Using this developed approach, a case study on upgrading fast-hydropyrolysis vapor of cellulose to higher molecular weight products was investigated. Levoglucosan and glycolaldehyde are major components from fast-hydropyrolysis of cellulose. This approach identied a reaction route that can upgrade these molecules to hydrocarbons with carbon number ranging from eight to 12 and this route has not been reported in the literature. The coupling of levoglucosan and glycolaldehyde requires a key intermediate, levoglucosenone, which is identied by this approach. Preliminary experimental results suggest that the proposed reactions are feasible and this serves as another validation for this approach. Other potential pathways to not only branched alkanes, but also substituted cycloalkanes and aromatics, were also identied. Molecules with those structures have been observed experimentally, and potential pathways to those molecules can provide insights for experimentalists as to how these products can form and which intermediates may lead to their formations. This approach has not only revealed unknown reaction routes, but also provided insights for experimentalists for analyzing complex systems.</div></div><div><br></div><div><div>Toward reduction of carbon losses toward char during fast pyrolysis, potential pathways toward char formation during fast pyrolysis were proposed. Investigating proposed char precursors identied using mass spectroscopy, several potential pathways toward the formation of these char precursors were obtained, which include initial insights to the potential driving force for the formation of these char precursors and, ultimately, char itself.</div></div><div><br></div><div><div>Going beyond fast pyrolysis, primary processes that have been developed in C3Bio along with several existing primary processes were considered in order to identify optimal biorenery congurations. This approach identied biorenery congurations with carbon effciencies from 60-64%. These congurations generate not only fuel type molecules, but also commodity chemicals that are being produced in a traditional renfiery. In addition, it is capable of providing these products at their current relative production rates in the United States. Other studies on biorefinery reported only 25-59% carbon effciency and generated mostly fuel-type molecules. Therefore, this approach not only indicates the appropriate reaction sequences, but also optimal utilization of carbon in biomass-derived molecules. This dissertation provides an initial roadmap toward sustainable production of fuels and chemicals from lignocellulosic biomass.</div></div><div><br></div><div><div>Considering that the transition to renewable energy is gradual and shale resource is an abundant fossil resource in the United States, opportunities to valorize shale gas condensate are explored. Recent shale gas boom has transformed the United States energy landscape. Most of the major shale basins are located in remote locations and historically non-gas producing regions. Therefore, many major shale basins regions are lacking the infrastructure to distribute the extracted gas into the rest of the US and particularly the Gulf Coast region. In this dissertation, shale gas catalytic upgrading processes were synthesized, designed, and simulated using Aspen Plus Simulation. Using Aspen Economic Analyzer, preliminary techno-economic analysis and evaluation of its economic potential were assessed at varying scales to assess its impact on the</div><div>United States chemical industry landscape.</div></div>

This dissertation primarily proposes data-driven methods to handle uncertainty in problems related to Enterprise-wide Optimization (EWO). Datadriven methods are characterized by the direct use of data (historical and/or forecast) in the construction of models for the uncertain parameters that naturally arise from real-world applications. Such uncertainty models are then incorporated into the optimization model describing the operations of an enterprise. Before addressing uncertainty in EWO problems, Chapter 2 deals with the integration of deterministic planning and scheduling operations of a network of batch plants. The main contributions of this chapter include the modeling of sequence-dependent changeovers across time periods for a unitspecific general precedence scheduling formulation, the hybrid decomposition scheme using Bilevel and Temporal Lagrangean Decomposition approaches, and the solution of subproblems in parallel. Chapters 3 to 6 propose different data analytics techniques to account for stochasticity in EWO problems. Chapter 3 deals with scenario generation via statistical property matching in the context of stochastic programming. A distribution matching problem is proposed that addresses the under-specification shortcoming of the originally proposed moment matching method. Chapter 4 deals with data-driven individual and joint chance constraints with right-hand side uncertainty. The distributions are estimated with kernel smoothing and are considered to be in a confidence set, which is also considered to contain the true, unknown distributions. The chapter proposes the calculation of the size of the confidence set based on the standard errors estimated from the smoothing process. Chapter 5 proposes the use of quantile regression to model production variability in the context of Sales & Operations Planning. The approach relies on available historical data of actual vs. planned production rates from which the deviation from plan is defined and considered a random variable. Chapter 6 addresses the combined optimal procurement contract selection and pricing problems. Different price-response models, linear and nonlinear, are considered in the latter problem. Results show that setting selling prices in the presence of uncertainty leads to the use of different purchasing contracts.

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