Multi-scale modelling and optimisation of sustainable chemical processes

This dissertation explores the process modelling and optimisation of chemical processes under sustainability criteria. Resting on process systems engineering techniques combined with life cycle assessment (LCA), we present implementation strategies to improve flowsheet performance and reduce environmental impacts from early design stages. We first address the relevance of sustainability assessments in the sector and present process and environmental modelling techniques available. Under the observation that chemical processes are subject to market, technical, and environmental fluctuations, we next present an approach to account for these uncertainties. Process optimisation is then tackled by combining surrogate modelling, objective-reduction, and multi-criteria decision analysis tools. The framework proved the enhancement of the assessments by reducing the use of computational resources and allowing the ranking of optimal alternatives based on the concept of efficiency. We finally introduce a scheme to assess sustainable performance at a multi-scale level, from catalysis development to planet implications. This approach aims to provide insights about the role of catalysis and establish priorities for process development, while also introducing absolute sustainability metrics via the concept of ‘Planetary boundaries’. Ultimately, this allows a clear view of the impact that a process incurs in the current and future status of the Earth. The capabilities of the methods developed are tested in relevant applications that address challenges in the sector to attain sustainable performance. We present how concepts like circular economy, waste valorisation, and renewable raw materials can certainly bring benefits to the industry compared to their fossil-based alternatives. However, we also show that the development of new processes and technologies is very likely to shift environmental impacts from one category to another, concluding that cross-sectorial cooperation will become essential to meet sustainability targets, such as those determined by the Sustainable Development Goals.Open Acces

Plant-Wide Diagnosis: Cause-and-Effect Analysis Using Process Connectivity and Directionality Information

Production plants used in modern process industry must produce products that meet stringent environmental, quality and profitability constraints. In such integrated plants, non-linearity and strong process dynamic interactions among process units complicate root-cause diagnosis of plant-wide disturbances because disturbances may propagate to units at some distance away from the primary source of the upset. Similarly, implemented advanced process control strategies, backup and recovery systems, use of recycle streams and heat integration may hamper detection and diagnostic efforts. It is important to track down the root-cause of a plant-wide disturbance because once corrective action is taken at the source, secondary propagated effects can be quickly eliminated with minimum effort and reduced down time with the resultant positive impact on process efficiency, productivity and profitability. In order to diagnose the root-cause of disturbances that manifest plant-wide, it is crucial to incorporate and utilize knowledge about the overall process topology or interrelated physical structure of the plant, such as is contained in Piping and Instrumentation Diagrams (P&IDs). Traditionally, process control engineers have intuitively referred to the physical structure of the plant by visual inspection and manual tracing of fault propagation paths within the process structures, such as the process drawings on printed P&IDs, in order to make logical conclusions based on the results from data-driven analysis. This manual approach, however, is prone to various sources of errors and can quickly become complicated in real processes. The aim of this thesis, therefore, is to establish innovative techniques for the electronic capture and manipulation of process schematic information from large plants such as refineries in order to provide an automated means of diagnosing plant-wide performance problems. This report also describes the design and implementation of a computer application program that integrates: (i) process connectivity and directionality information from intelligent P&IDs (ii) results from data-driven cause-and-effect analysis of process measurements and (iii) process know-how to aid process control engineers and plant operators gain process insight. This work explored process intelligent P&IDs, created with AVEVA® P&ID, a Computer Aided Design (CAD) tool, and exported as an ISO 15926 compliant platform and vendor independent text-based XML description of the plant. The XML output was processed by a software tool developed in Microsoft® .NET environment in this research project to computationally generate connectivity matrix that shows plant items and their connections. The connectivity matrix produced can be exported to Excel® spreadsheet application as a basis for other application and has served as precursor to other research work. The final version of the developed software tool links statistical results of cause-and-effect analysis of process data with the connectivity matrix to simplify and gain insights into the cause and effect analysis using the connectivity information. Process knowhow and understanding is incorporated to generate logical conclusions. The thesis presents a case study in an atmospheric crude heating unit as an illustrative example to drive home key concepts and also describes an industrial case study involving refinery operations. In the industrial case study, in addition to confirming the root-cause candidate, the developed software tool was set the task to determine the physical sequence of fault propagation path within the plant. This was then compared with the hypothesis about disturbance propagation sequence generated by pure data-driven method. The results show a high degree of overlap which helps to validate statistical data-driven technique and easily identify any spurious results from the data-driven multivariable analysis. This significantly increase control engineers confidence in data-driven method being used for root-cause diagnosis. The thesis concludes with a discussion of the approach and presents ideas for further development of the methods

Exploitation of Biomass for Applications in Sustainable Materials Science

Biorefinery may be defined as the process of accessing chemical commodities from living systems; consequently, biomass becomes the antecedent for renewable resources through biorefinery. Advantages to this process over petroleum refinery include: (1) increased potential for sustainable products, (2) increased diversity in chemical structure including heterocycles, and (3) potential for regional resource independence. Despite these clear advantages, adoption of biorefined commodities can be limited by the risk associated with small initial application portfolios and concomitant uncertainties. The strategies adopted by our dynamic and collaborative research team entail continuous engagement of those issues by: (1) preparing renewable polymers, (2) chemical diversification of biomass-derived platform chemicals, (3) direct modification of biopolymers, and (4) development of petroleum replacements. Battling the inveterate proclivity towards portents of gloom need not solely justify investigations into biorenewable feedstock chemicals; the ramifications of bioinspired molecular inquiry create opportunities to go beyond mere sustainability through innovation. This dissertation includes specific examples which illustrate utilization of three types of biomass: (1) oil seeds, (2) lignin, and (3) carbohydrates. Each class of biomass-derived materials offered unique advantages as well as challenges associated with their varied structures. The presentation has been divided into five sections: (1) biomass, sustainable chemistry and design thinking; (2) styrene replacements and their application in renewable vinyl ester thermosets; (3) catalyst-free lignin valorization by acetoacetylation; (4) chemical diversification of 5-(hydroxymethyl)furfural; (5) valorization of cellulose-derivable platform chemicals by cycloaddition with a potentially bioderivable reactive intermediate: benzyne.National Science Foundation (NSF) (Grants IIA-1330840 and IIA-1355466)ND-EPSCoR (Doctoral Dissertation Award