Dynamic optimization of a novel radial-flow, spherical-bed methanol synthesis reactor in the presence of catalyst deactivation using Differential Evolution (DE) algorithm

2009 ◽  
Vol 34 (15) ◽  
pp. 6221-6230 ◽  
Author(s):  
M.R. Rahimpour ◽  
P. Parvasi ◽  
P. Setoodeh
Keyword(s):  
Differential Evolution ◽  
Dynamic Optimization ◽  
Methanol Synthesis ◽  
Radial Flow ◽  
Catalyst Deactivation ◽  
De Algorithm

scholarly journals Dynamic optimization of methanol synthesis section in the dual type configuration to increase methanol production

2019 ◽  
Vol 74 ◽  
pp. 90
Author(s):  
Samane Masoudi ◽  
Mohammad Farsi ◽  
Mohammad Reza Rahimpour
Keyword(s):  
Dynamic Optimization ◽  
Methanol Synthesis ◽  
Catalyst Deactivation ◽  
Dynamic Condition ◽  
Production Capacity ◽  
Operating Conditions ◽  
Second Step ◽  
Methanol Production ◽  
Equilibrium Conversion ◽  
Plant Data

The main object of this research is dynamic modeling and optimization of the methanol synthesis section in the dual type configuration considering catalyst deactivation to improve methanol production capacity. In the methanol unit, deactivation of CuO/ZnO/Al2O3 catalyst by sintering and low equilibrium conversion of reactions limit the production capacity, and changing operating temperature is a practical solution to overcome the production decay. In the first step, the considered process is modeled based on the mass and energy balance equations at dynamic condition. To prove the accuracy of developed model, the simulation results are compared with the plant data at the same operating conditions. In the second step, a dynamic optimization problem is formulated, and the optimal trajectories of manipulated variables are determined considering methanol production rate as the objective function. Finally, the performance of optimized process is compared with the conventional system at the same design conditions. The results show that operating at the optimal conditions increases methanol production capacity about 6.45%.

scholarly journals Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110144
Author(s):  
Qianqian Zhang ◽  
Daqing Wang ◽  
Lifu Gao
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Inverse Kinematics ◽  
Differential Evolution Algorithm ◽  
Weight Coefficient ◽  
Adaptive Mutation ◽  
Feasible Region ◽  
Serial Manipulators ◽  
De Algorithm ◽  
Self Adaptive

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

USING AN IMPROVED BEE MEMORY DIFFERENTIAL EVOLUTION ALGORITHM FOR PARAMETER ESTIMATION TO SIMULATE BIOCHEMICAL PATHWAYS

2014 ◽  
Vol 22 (01) ◽  
pp. 101-121 ◽  
Author(s):  
CHUII KHIM CHONG ◽  
MOHD SABERI MOHAMAD ◽  
SAFAAI DERIS ◽  
MOHD SHAHIR SHAMSIR ◽  
LIAN EN CHAI ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Estimation Algorithm ◽  
Convergence Time ◽  
Unknown Parameters ◽  
Estimation Algorithms ◽  
Bee Colony ◽  
De Algorithm ◽  
Cell Cycle Pathway ◽  
Parameter Values

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder–Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

scholarly journals Differential Evolution with Population and Strategy Parameter Adaptation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
V. Gonuguntla ◽  
R. Mallipeddi ◽  
Kalyana C. Veluvolu
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Optimization Problems ◽  
Search Space ◽  
Benchmark Problems ◽  
Parameter Adaptation ◽  
De Algorithm ◽  
Probabilistic Technique ◽  
Number Of Individuals ◽  
Strategy Parameters

Differential evolution (DE) is simple and effective in solving numerous real-world global optimization problems. However, its effectiveness critically depends on the appropriate setting of population size and strategy parameters. Therefore, to obtain optimal performance the time-consuming preliminary tuning of parameters is needed. Recently, different strategy parameter adaptation techniques, which can automatically update the parameters to appropriate values to suit the characteristics of optimization problems, have been proposed. However, most of the works do not control the adaptation of the population size. In addition, they try to adapt each strategy parameters individually but do not take into account the interaction between the parameters that are being adapted. In this paper, we introduce a DE algorithm where both strategy parameters are self-adapted taking into account the parameter dependencies by means of a multivariate probabilistic technique based on Gaussian Adaptation working on the parameter space. In addition, the proposed DE algorithm starts by sampling a huge number of sample solutions in the search space and in each generation a constant number of individuals from huge sample set are adaptively selected to form the population that evolves. The proposed algorithm is evaluated on 14 benchmark problems of CEC 2005 with different dimensionality.

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