Optimisation of complex distillation colomn systems using rigorous models

Since distillation is still the most widely used separation technique used in the petrochemical industry, optimisation of these unit operations are important to minimise costs and maximise production. This thesis focuses on the development of a tool using rigorous non-equilibrium distillation models to optimise complex columns. Non-equilibrium distillation models are usually avoided in optimisation studies due to the time required to solve them, but this has been overcome by using a technique called orthogonal collocation in which the profiles in the columns are represented by polynomials of a lower order than would be required normally. This significantly reduces the process times and makes the use of non-equilibrium models a possibility in optimisation studies. The orthogonal collocation technique was applied to a packed distillation column model and shown to be effective in modelling the system. A system consisting of a distillation column with integrated external side reactors was chosen as a case study to investigate the use of the methods. These systems have been shown to be effective in certain circumstances in literature, when comparing them to other forms of process intensification, such as reactive distillation. The toluene disproportionation reaction was considered as a potential use for the technology and the optimisation tool was used to find optimum system configurations for achieving maximum toluene conversions and minimum costs. Nonlinear programming techniques were used initially to optimise these systems, but due to the discontinuities associated with multiple side streams, they were replaced by a genetic algorithm. Various system configurations were identified as achieving maximum conversions and minimum costs. These results were used in a comparison with results obtained from a literature study and the results showed significant promise. Unfortunately, the two studies did not have enough in common to truly produce a comprehensive result. This iv lead to further comparisons with another system using the same information. The results obtained in the toluene disproportionation case study showed that there was some possible benefits for using the side reactor systems, but the conventional system was still 30 and 60% cheaper in terms of capital and utility costs respectively. Another case study was investigated that looked at the synthesis of methyl acetate from acetic acid and methanol. The packed collocation model was used as a comparison with another investigation performed in literature (using equilibrium distillation models). Both showed comparable results, but still had significant differences. Costs were also compared between the side reactor system and a more conventional system for methyl acetate synthesis. The side reactor systems were found to be more cost effective than the conventional system. Additionally, an increase in the number of external reactors resulted in lower utility costs (mainly as a result of lower flow rates in the side streams). Overall, the reaction and process conditions are important considerations when deciding whether or not to use a side reactor system. For the gas phase toluene disproportionation reaction, the side reactor systems were not cost effective, when compared to the conventional system. However, the liquid phase methyl acetate reaction proved to be more conducive to side reactor systems in terms of cost. This thesis has shown the applicability of using rigorous disequilibrium distillation models in optimisation studies. The side reactor systems have been found to be complex systems that require a holistic approach to find optimum configurations instead of optimising individual process units

Optimization of Process Flowsheets through Metaheuristic Techniques

This book presents a multi-objective optimization framework for optimizing chemical processes. The proposed framework implements a link between process simulators and metaheuristic techniques. The proposed approach is general, and there can be used any process simulator and any metaheuristic technique. This book shows how to implement links between different process simulators such as Aspen Plus®, HYSYS®, SuperPro Designer®, and others, linked to metaheuristic techniques implemented in Matlab®, Excel®, C++, or other programs. This way, the proposed framework allows optimizing any process flowsheet implemented in the process simulator and using the metaheuristic technique, and this way the numerical complications through the optimization process can be eliminated. Furthermore, the proposed framework allows using the thermodynamic, design, and constitutive equations implemented in the process simulator to implement any process

A multi-objective genetic algorithm for the design of pressure swing adsorption

Pressure Swing Adsorption (PSA) is a cyclic separation process, more advantageous over other separation options for middle scale processes. Automated tools for the design of PSA processes would be beneficial for the development of the technology, but their development is a difficult task due to the complexity of the simulation of PSA cycles and the computational effort needed to detect the performance at cyclic steady state. We present a preliminary investigation of the performance of a custom multi-objective genetic algorithm (MOGA) for the optimisation of a fast cycle PSA operation, the separation of air for N2 production. The simulation requires a detailed diffusion model, which involves coupled nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA to handle this complex problem has been assessed by comparison with direct search methods. An analysis of the effect of MOGA parameters on the performance is also presented

A Comprehensive Study Of Esterification Of Free Fatty Acid To Biodiesel In a Simulated Moving Bed System

Simulated Moving Bed (SMB) systems are used for separations that are difficult using traditional separation techniques. Due to the advantage of adsorption-based chromatographic separation, SMB has shown promising application in petrochemical and sugar industries, and of late, for chiral drug separations. In recent years, the concept of integration of reaction and in-situ separation in a single unit has achieved considerable attention. The simulated moving bed reactor (SMBR) couples both these unit operations bringing down the operation costs while improving the process performance, particularly for products that require mild operating conditions. However, its application has been limited due to complexity of the SMBR process. Hence, to successfully implement a reaction in SMB, a detailed understanding of the design and operating conditions of the SMBR corresponding to that particular reaction process is necessary. Biodiesel has emerged has a viable alternative to petroleum-based diesel as a renewable energy source in recent years. Biodiesel can be produced by esterification of free fatty acids (present in large amounts in waste oil) with alcohol. The reaction is equilibrium-limited, and hence, to achieve high purity, additional purification steps increases the production cost. Therefore, combining reaction and separation in SMBR to produce high purity biodiesel is quite promising in terms of bringing down the production cost. In this work, the reversible esterification reaction of oleic acid with methanol catalyzed by Amberlyst 15 resin to form methyl oleate (biodiesel) in SMBR has been investigated both theoretically and experimentally. First, the adsorption and kinetic constants were determined for the biodiesel synthesis reaction by performing experiments in a single column packed with Amberlyst 15, which acts as both adsorbent and catalyst. Thereafter, a rigorous model was used to describe the dynamic behaviour of multi-column SMBR followed by experimental verification of the mathematical model. Sensitivity analysis is done to determine robustness of the model. Finally, a few simple multi-objective optimization problems were solved that included both existing and design-stage SMBRs using non-dominated sorting genetic algorithm (NSGA). Pareto-optimal solutions were obtained in both cases, and moreover, it was found that the performance of the SMBR could be improved significantly under optimal operating conditions

Logic hybrid simulation-optimization algorithm for distillation design

In this paper, we propose a novel algorithm for the rigorous design of distillation columns that integrates a process simulator in a generalized disjunctive programming formulation. The optimal distillation column, or column sequence, is obtained by selecting, for each column section, among a set of column sections with different number of theoretical trays. The selection of thermodynamic models, properties estimation etc., are all in the simulation environment. All the numerical issues related to the convergence of distillation columns (or column sections) are also maintained in the simulation environment. The model is formulated as a Generalized Disjunctive Programming (GDP) problem and solved using the logic based outer approximation algorithm without MINLP reformulation. Some examples involving from a single column to thermally coupled sequence or extractive distillation shows the performance of the new algorithm.Spanish Ministry of Science and Innovation (CTQ2012-37039-C02-02)

Gibbs Energy Minimization Using Simulated Annealing for Two-phase Equilibrium Calculations in Reactive Systems

Phase equilibrium calculations in systems subject to chemical reactions are involved in the design, synthesis and optimization of reactive separation processes. Until now, several methods have been developed to perform simultaneously physical and chemical equilibrium calculations. However, published methods may face numerical difficulties such as variable initialization dependence, divergence and convergence to trivial solutions or unstable equilibrium states. Besides, these methods generally use conventional composition variables and reactions extents as unknowns which directly affect the numerical implementation, reliability and efficiency of solving strategies. The objective of this work is to introduce and test an alternative approach to perform Gibbs energy minimization in phase equilibrium problems for reactive systems. Specifically, we have employed the transformed composition variables of Ung and Doherty and the stochastic optimization method Simulated Annealing for two-phase equilibrium calculations in reacting systems. Performance of this strategy has been tested using several benchmark problems and results show that proposed approach is generally suitable for the global minimization of transformed Gibbs energy in reactive systems with two-phase equilibrium