Multi-objective Dynamic Optimization for Pressure Swing Adsorption

Nitin Padhiya

Optimal startup operation of a pressure swing adsorption

Pressure swing adsorption (PSA) is a gas separation and purification technology for high purity and small throughput. There exists literature on simulation and optimization of PSA operation; however most of it is based on cyclic steady state (CSS) conditions. There is scant literature on simulations of startup operation. We present the problem formulations of optimal startup in PSA for separating H2-CO2 in this work. Two formulations were considered for the startup, one with the consideration of only the CSS conditions in the objective function and the other one by considering both, the transient and CSS conditions in the objective function. The performance of both the operations was measured in terms of the number of cycles to attain the CSS (NCSS). The consideration of transient conditions demand optimizing the transient profile (until the CSS is achieved) of the manipulated variable, which requires large computational expense, however providing greater benefits in terms of reduced NCSS.by Parth Sinha and Nitin Padhiya

Fast mesh-sorting in multi-objective optimization

by Narendra Madhavlaland Patel And Nitin Padhiyar

Optimal operation of a fed batch reactor for the synthesis of a recombinant β-1,3-glucanase

by Punit Rawat and Nitin Padhiya

Multi-objective optimal control study of Fed-Batch Bio-Reactor

Evolutionary algorithms are widely used for dynamic optimization problems of fed-batch bio-reactors for productivity-yield maximization by optimizing the substrate feed recipe. However, this is usually done for a fixed fed-batch time. Conventionally, the optimum fed-batch time is computed by solving several single objective dynamic optimization problems for different fed-batch time. Since this approach is computationally quite expensive, we propose a Multi-Objective Optimization (MOO) problem formulation to find the optimum fed-batch time for maximizing productivity and/or yield. Such an MOO approach is expected to save significant computational efforts. To demonstrate the proposed MOO implementations for dynamic optimization of fed-batch bio-reactors, secreted protein production is considered as a case study. Specifically, four distinct objectives, namely productivity, yield, fed-batch time, and endpoint substrate concentration are considered in this work. An evolutionary multi-objective differential evolution algorithm is used for solving the MOO problems.by Narendra Patel and Nitin Padhiya

Alien Genetic Algorithm for Exploration of Search Space

Genetic Algorithm (GA) is a widely accepted population based stochastic optimization technique used for single and multi objective optimization problems. Various versions of modifications in GA have been proposed in last three decades mainly addressing two issues, namely increasing convergence rate and increasing probability of global minima. While both these. While addressing the first issue, GA tends to converge to a local optima and addressing the second issue corresponds the large computational efforts. Thus, to reduce the contradictory effects of these two aspects, we propose a modification in GA by adding an alien member in the population at every generation. Addition of an Alien member in the current population at every generation increases the probability of obtaining global minima at the same time maintaining higher convergence rate. With two test cases, we have demonstrated the efficacy of the proposed GA by comparing with the conventional GA.by Narendra Patel and Nitin Padhiya

Kinetic Study of Bechamp Process for P-Nitrotoluene Reduction to P-Toluidine

Bechamp process is a well-known process for the reduction of aromatic nitro compounds using zero valent iron powder and acid. Reduction of p-nitro toluene (PNT) to p-toluidine (PT) is a process of three steps in series, namely adsorption of PNT on the iron surface, surface reaction of PNT to PT and desorption of the product from the iron surface. Reduction of PNT to PT by Bechamp process has carried out in a 500 ml batch reactor in this work. Gas Chromatograph (GC) is used for the sample analysis. A GC method has been developed with toluene as the solvent for determining the compositions of various reaction components. In this work, we have carried out experiments to find out the limiting step for PNT reduction. We have considered selectivity of PT as performance criteria in this study at various operating conditions. We also present the effect of rpm on rate constant and present the mathematical model for the same.by Vivek Popat and Nitin Padhiyarby Vivek Popat and Nitin Padhiya

Box-complex assisted genetic algorithm for optimal control of batch reactor

by Narendra Madhavlal Patel And Nitin Padhiyar

Modified genetic algorithm using box complex method: application to optimal control problems

Genetic algorithm (GA) is a popular stochastic optimization technique for past couple of decades and has been successfully applied to numerous applications of single and multi-objective optimization problems. Various modifications in GA are proposed in open literature to increase convergence rate and probability of obtaining global minimum by increasing population diversity. Box Complex is a gradient free optimization method having good convergence property. To enhance convergence property of GA, we in this work propose an extension of GA by combining the global search property of GA with a convergence property of Box Complex method. We add one or more population members created by Box Complex method using the current population and replace the equal number of worst population members every generation. A comparison study of the proposed GA with conventional GA and widely accepted jumping gene GA (JG GA) is presented in this work. We have considered two benchmark optimization functions, namely Rosenbrock's and Ackley's Path function. We also carry out the comparison of GAs for three optimal control problems. One of them is the maximization of product concentration with multiple reactions in a batch reactor. Minimization of the off-spec product during product grade transition in a polymerization reactor is considered as the second optimal control problem. The third test application is optimal control of a non-isothermal plug flow reactor. There are two user defined parameters in the proposed algorithm, namely number of Box Complex Members (BCM), and expansion/contraction factor α. Effect of both these parameters on the convergence profile have been presented in this work for the proposed GA. A statistical summary of ten simulation runs for the proposed GA, JG GA, and conventional GA has been discussed for each of the five applications.by Narendra Patel and Nitin Padhiya

Profile control in distributed parameter systems using lexicographic optimization based MPC

Process equipment that exhibits significant spatial variation of system properties, such as temperature or concentration in a fixed bed reactor, are typically modeled as distributed parameter systems. While some properties of the final product exiting the equipment may depend on the states concerning the endpoint, others may be a function of the history of processing within the equipment. In such instances, control of the spatial property profile may be beneficial. In this work, we explore the idea of profile control using extended MPC and outline the additional challenges that must be addressed in this context. In case that the target profile is unachievable, we present an MPC formulation that uses lexicographic optimization to prioritize the different sections of the profile. Simulation of a simple representative system namely a hypothetical plug flow reactor is used to demonstrate that the lexicographic optimization based MPC provides a systematic approach to profile control and spans between the endpoint control strategy and the whole profile control strategy. The benefits of lexicographic optimization based MPC were also demonstrated on a large-scale distributed parameter system of industrial size, namely the continuous pulp digester.© Elsevie