A DNA based genetic algorithm for parameter estimation in the hydrogenation reaction

Inspired by the mechanism of the biological DNA, a DNA based genetic algorithm (DNA-GA) is proposed to determine the kinetic parameters for the hydrogenation reaction. The considered chemical process contains five reactions and 25 unknown parameters. The DNA-GA uses the DNA encoding method to represent the potential parameters and genetic operators inspired from the biological DNA are designed to find the global optimum. The study on the performance for typical benchmark functions shows that the DNA-GA outperforms the other two methods in both convergence speed and accuracy. Based on the operating data gathered from an industrial hydrogenation unit, 25 parameters are obtained by the DNA-GA and the kinetic model for the hydrogenation reaction is established. To verify the validity of the established model, another four groups of data are used to test the established model and two previously reported models. The comparison results show that the sum of square relative errors of the model obtained by the DNA-GA is the least of the test models, and its prediction is in good agreement with the practical operating data.     

DNA computing based RNA genetic algorithm with applications in parameter estimation of chemical engineering processes

Based on RNA genetic operations and DNA sequence model under selection and mutation, an electronic RNA genetic algorithm (RNA-GA) with improved crossover and mutation operator is proposed. The proposed algorithm can be implemented on real biochemical reaction after simple transition, thus, the brute force method of DNA computing can be broken. The convergence analysis of the proposed algorithm shows that RNA-GA with elitist strategy can converge in probability to the global optimum. Comparisons of RNA-GA with standard genetic algorithm (SGA) for typical test functions show the advantages and efficiency of the proposed algorithm. As illustrations, the RNA-GA is implemented on parameter estimation of a heavy oil thermal cracking 3-lumping model and a fluid catalytic cracking unit (FCCU) main fractionator. In both cases, it is shown that the methodology is effective in parameter estimation of chemical processes.     

A hybrid genetic algorithm for efficient parameter estimation of large kinetic models

The development of predictive models is a time consuming, knowledge intensive, iterative process where an approximate model is proposed to explain experimental data, the model parameters that best fit the data are determined and the model is subsequently refined to improve its predictive capabilities. Ascertaining the validity of the proposed model is based upon how thoroughly the parameter search has been conducted in the allowable range. The determination of the optimal model parameters is complicated by the complexity/non-linearity of the model, potentially large number of equations and parameters, poor quality of the data, and lack of tight bounds for the parameter ranges. In this paper, we will critically evaluate a hybrid search procedure that employs a genetic algorithm for identifying promising regions of the solution space followed by the use of an optimizer to search locally in the identified regions. It has been found that this procedure is capable of identifying solutions that are essentially equivalent to the global optimum reported by a state-of-the-art global optimizer but much faster. A 13 parameter model that results in 60 differential-algebraic equations for propane aromatization on a zeolite catalyst is proposed as a more challenging test case to validate this algorithm. This hybrid technique has been able to locate multiple solutions that are nearly as good with respect to the “sum of squares” error criterion, but imply significantly different physical situations.     

A novel RNA genetic algorithm for parameter estimation of dynamic systems

Inspired by RNA molecular structure and operators, a novel RNA genetic algorithm (NRNA-GA) with RNA encoding and operators is proposed for addressing parameter estimation problems of dynamic systems. It adopts nucleotides based encoding and some RNA molecular operators, such as permutation operator and stem-loop operator, which is different from conventional genetic algorithms (GAs). An adaptive mutation rate is also used to guard against stalling at local peak. In order to overcome the drawbacks of premature convergence of GAs, a type of special fitness function incorporating objective function values with Euclidean spaces distance is introduced, which leads the population to maintain its diversity and the algorithm to jump out of local optima. A simple direct search method is incorporated into the NRNA-GA to improve local search performance. Numerical experiments about benchmark functions and real-world parameter estimation problems in dynamic systems demonstrate the efficiency and effectiveness of the proposed optimization algorithm.     

Optimization of short-time gasoline blending scheduling problem with a DNA based hybrid genetic algorithm

Gasoline blending is a key process in the petroleum refinery industry posed as a nonlinear optimization problem with heavily nonlinear constraints. This paper presents a DNA based hybrid genetic algorithm (DNA-HGA) to optimize such nonlinear optimization problems. In the proposed algorithm, potential solutions are represented with nucleotide bases. Based on the complementary properties of nucleotide bases, operators inspired by DNA are applied to improve the global searching ability of GA for efficiently locating the feasible domains. After the feasible region is obtained, the sequential quadratic programming (SQP) is implemented to improve the solution. The hybrid approach is tested on a set of constrained nonlinear optimization problems taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm. The recipes of a short-time gasoline blending problem are optimized by the hybrid algorithm, and the comparison results show that the profit of the products is largely improved while achieving more satisfactory quality indicators in both certainty and uncertainty environment.     

Modeling and optimization of an industrial hydrocracking unit to improve the yield of diesel or kerosene

Hydrocracking is used in the petroleum industry to convert low-quality feedstocks into highly-valued transportation fuels. This process is the best source of low-sulfur and low-aromatics diesel fuel as well as high-smoke point jet fuel. Many approaches have been proposed for solving optimization of hydrocracking units in the last decades, but they usually neglect the reaction in hydrotreater where hydrocarbon cracking often occurs, thus leading to suboptimal solutions in industrial problems. Unlike existing literature, this paper considers the hydrocarbon cracking reactions in hydrotreater and hydrocracker simultaneously. The models are based on energy balance, mass balance and a discrete lumped model approaches for kinetic modeling. Before optimization, the properties of feedstock are predicted with ASPEN PLUS by using laboratory data from the refinery, and then the model parameters are estimated with genetic algorithm (GA) based on industrial data and validated by comparing the simulating results with industrial data. To improve the yield of the lighter products, the operation conditions are optimized by GA and Sequential Quadratic Programming (SQP). The yields of the diesel or kerosene increase with the proposed approach.     

Evaluation of Covariance Matrix Adaptation Evolution Strategy,Shuffled Complex Evolution and Firefly Algorithms for phase stability,phase equilibrium and chemical equilibrium problems

Phase equilibrium calculations and phase stability analysis of reactive and non-reactive systems play a significant role in the simulation, design and optimization of reaction and separation processes in chemical engineering. These challenging problems, which are often multivariable and non-convex, require global optimization methods for solving them. Stochastic global optimization algorithms have shown promise in providing reliable and efficient solutions for these thermodynamic problems. In this study, we evaluate three alternative global optimization algorithms for phase and chemical equilibrium calculations, namely, Covariant Matrix Adaptation-Evolution Strategy (CMA-ES), Shuffled Complex Evolution (SCE) and Firefly Algorithm (FA). The performance of these three stochastic algorithms was tested and compared to identify their relative strengths for phase equilibrium and phase stability problems. The phase equilibrium problems include both multi-component systems with and without chemical reactions. FA was found to be the most reliable among the three techniques, whereas CMA-ES can find the global minimum reliably and accurately even with a smaller number of iterations.