Reactor Network Synthesis Using Coupled Genetic Algorithm with the Quasi-linear Programming Method

This research is an attempt to develop a new procedure for the synthesis of reactor networks (RNs) using a genetic algorithm (GA) coupled with the quasi-linear programming (LP) method. The GA is used to produce structural configuration, whereas continuous variables are handled using a quasi-LP formulation for finding the best objective function. Quasi-LP consists of LP together with a search loop to find the best reactor conversions (xi), as well as split and recycle ratios (yi). Quasi-LP replaces the nonlinear programming (NLP) problem, and is easier to solve. To prevent complexity and ensure an optimum solution, two types of ideal reactors, namely plug flow reactor (PFR) and continuous stirred tank reactor (CSTR), were considered in the network. Since PFRs require the introduction of differential equations into the problem formulation, a CSTR cascade was used instead in order to eliminate differential equations. To demonstrate the effectiveness of the proposed method, three reactor-network synthesis case studies are presented

Numerical Formulations For Attainable Region Analysis

Student Number : 9611112G - PhD thesis - School of Chemical and Metallurgical Engineering - Faculty of Engineering and the Built EnvironmentAttainable Region analysis is a chemical process synthesis technique that enables a design engineer to find process unit configurations that can be used to identify all possible outputs, by considering only the given feed specifications and permitted fundamental processes. The mathematical complexity of the attainable regions theory has so far been a major drawback in the implementation of this powerful technique into standard process design tools. In the past five years researchers focused on developing systematic methods to automate the procedure of identifying the set of all possible outputs termed the Attainable Regions. This work contributes to the development of systematic numerical formulations for attainable region analysis. By considering combinations of fundamental processes of chemical reaction, bulk mixing and heat transfer, two numerical formulations are proposed as systematic techniques for automation of identifying optimal process units networks using the attainable region analysis. The first formulation named the recursive convex control policy (RCC) algorithm uses the necessary requirement for convexity to approximate optimal combinations of fundamental processes that outline the shape of the boundary of the attainable regions. The recursive convex control policy forms the major content of this work and several case studies including those of industrial significance are used to demonstrate the efficiency of this technique. The ease of application and fast computational run-time are shown by assembling the RCC into a user interfaced computer application contained in a compact disk accompanying this thesis. The RCC algorithm enables identifying solutions for higher dimensional and complex industrial case studies that were previously perceived impractical to solve. The second numerical formulation uses singular optimal control techniques to identify optimal combinations of fundamental processes. This formulation also serves as a guarantee that the attainable region analysis conforms to Pontryagin’s maximum principle. This was shown by the solutions obtained using the RCC algorithm being consistent with those obtained by singular optimal control techniques

Modelling, safety verification and design of discrete/continuous processing systems.

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Improving batch reactors using attainable regions: Towards automated construction of the attainable region and its application to batch reactors

The Attainable Region is the set of all achievable states, for all possible reactor configurations, obtained by reaction and mixing alone. It is a geometric method that is effective in addressing problems found in reactor network synthesis. For this reason, Attainable Region theory assists towards a better understanding of systems of complex reaction networks and the issues encountered by these systems. This thesis aims to address two areas in Attainable Region theory: 1. To help improve the design and operation of batch reactors using Attainable Regions. 2. To further advance knowledge and understanding of efficient Attainable Region construction methods. Using fundamental concepts of mixing and attainability established by Attainable Region theory, a graphical method of identifying opportunities for improving the production rate from batch reactors is first presented. It is found that by modifying the initial concentration of the batch, overall production performance may be improved. This may be achieved in practice by retaining a fraction of the final product volume and mixing with fresh feed material for subsequent cycles. This result is counter-intuitive to the normal method of batch operation. Bypassing of feed may also be used to improve production rate for exit concentrations not associated with the optimal concentration. The graphical approach also allows optimisation of batches where only experimental data are given. An improved method of candidate Attainable Region construction, based on an existing bounding hyperplanes approach is then presented. The method uses a plane rotation about existing extreme points to eliminate unachievable regions from an initial bounding set. The algorithm is shown to be faster and has been extended to include construction of candidate Attainable Regions involving non-isothermal kinetics in concentration and concentration-time space. With the ideas obtained above, the application of Attainable Regions to batch reactor configurations is finally presented. It is shown that with the appropriate transformation, results developed from a continuous Attainable Region may be used to form a related batch structure. Thus, improvement of batch reactor structures is also possible using Attainable Regions. Validation of candidate Attainable Regions is carried out with the construction algorithm developed in this work

A computer tool for the generation of process models from a physical description

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Integrated modular microfluidic system for forensic Alu DNA typing

Driven by the numerous applications of genome-related research, fully integrated microfluidic systems have been developed that have advanced the capabilities of molecular and, in particular, genetic analyses. A brief overview on integrated microfluidic systems for DNA analysis is given in Chapter 1 followed by a report on micro-capillary electrophoresis (µCE) of Alu elements with laser-induced fluorescence (LIF) detection, in which the monomorphic Alu insertions on the X and Y chromosomes were utilized to detect male DNA in large female DNA background (Y: X = 1:19) without cell sorting prior to the determination. The polymorphic Alu loci with known restricted geographical distribution were used for ethnicity determination. A valveless integrated microsystem that consists of three modules is discussed as well: (1) A solid-phase extraction (SPE) module microfabricated on polycarbonate, for DNA extraction from whole cell lysates (extraction bed capacity ~209 ±35.6 ng/cm² of total DNA). (2) A continuous-flow polymerase chain reaction (CFPCR) module fabricated in polycarbonate (Tg ~150 ºC) in which selected gene fragments were amplified using biotin and fluorescently-labeled primers accomplished by continuously shuttling small packets of PCR reagents and template through isothermal zones. (3) µCE module fabricated in poly(methylmethacrylate), which utilized a bioaffinity selection and purification bed (2.9-µL) to preconcentrate and purify the PCR products generated from the CFPCR module prior to µCE. Biotin-labeled CFPCR products were hydrostatically pumped through the streptavidin-modified bed where they were extracted onto the surface of the poly(methylmethacrylate) micropillars (50-µm width; 100-µm height; total surface area of ~117 mm²). This SPE process demonstrated high selectivity for biotinylated amplicons and utilized the strong streptavidin/biotin interaction (Kd =10-15M) to generate high recoveries. The SPE selected CFPCR products were thermally denatured and single stranded DNA released for size-based separations and LIF detection. The multiplexed SPE-CFPCR-µCE yielded detectable fluorescence signal (S/N≥3; LOD ~75 cells) for Alu DNA amplicons for gender and ethnicity determinations with a separation efficiency of ~1.5 x105 plates/m. Compared to traditional cross-T injection procedures typically used for µCE, the affinity preconcentration and injection procedure generated signal enhancements of 17-40 fold, critical for CFPCR thermal cyclers due to Taylor dispersion associated with their operation

Linear and Non-Linear Programming Techniques for System Identification and Green Engineering Applications

This work focuses on linear and non-linear programming techniques for feasibility assessment, synthesis, and optimization of process networks. Analytical techniques, dimensionality reduction, convexification strategies, and functional analysis are developed to identify global optima and to enhance the efficiency of their computation. Green engineering, especially the hydrogen economy, is the primary motivation of this work. To this end, these strategies are implemented in identification of the attainable region for separator networks, optimization of compressors and coolers in series delivering hydrogen at high pressure, and minimum utility cost of an energetically-enhanced steam methane reforming process in the presence of carbon tax legislation.Pursuant to feasibility assessment of systems with respect to delivery of pure compounds, the applicability of the Infinite DimEnsionAl State-space (IDEAS) framework is extended to attainable region (AR) identification for separator networks. The AR for a water/methanol/acetone mixture involving one network feed stream with known species molar fractions and three network outlet streams at 1 atm pressure. The binary methanol/acetone system exhibits a minimum-boiling azeotrope at 79.07% mole fraction of acetone at 328.5 K. The IDEAS-generated AR successfully identified that the binary methanol/acetone azeotrope was inside the AR, and thus demonstrated there exists a separator network that can bypass the azeotrope. In the exploration of hydrogen as an alternative energy source, the minimum operating cost and minimum capital cost problems for a system of compressors and coolers in series that bring a gas with constant compressibility factor from a specified initial state (T0, P0) to a specified final state (T0, Pn). Through mathematical proof, the dimensionality of the optimization problems is reduced and analytical properties of the compressor outlet temperatures, when either operating costs or capital costs dominate, are established. A case study involving hydrogen compression was done to illustrate the methods, and shows the global optimum achieves cost savings of up to 13% from conventional designs. Lastly, a novel method for solution of linear parametric programming problems is proposed based on the concept of dimensionality reduction – this allows the analytic quantification of the optimum objective function value and associated optimum variable vector. Regions in carbon/renewable utility cost coefficient ratio space are identified, in which one technology is superior over the other. EESMR is shown to be preferable in the presence of significant levels of taxation on the use of natural gas as fuel