Aiyun Hu¹, Sunli Cong^1,*, Jian Ding², Yao Cheng¹, Enock Mpofu³

Computer Systems Science and Engineering, Vol.38, No.1, pp. 65-77, 2021, DOI:10.32604/csse.2021.016404

Abstract In the fed-batch cultivation of Saccharomyces cerevisiae, excessive glucose addition leads to increased ethanol accumulation, which will reduce the efficiency of glucose utilization and inhibit product synthesis. Insufficient glucose addition limits cell growth. To properly regulate glucose feed, a different evolution algorithm based on self-adaptive control strategy was proposed, consisting of three modules (PID, system identification and parameter optimization). Performance of the proposed and conventional PID controllers was validated and compared in simulated and experimental cultivations. In the simulation, cultivation with the self-adaptive control strategy had a more stable glucose feed rate and concentration, more stable ethanol concentration around the… More >

Qamar Abbas^a, Jamil Ahmad^b, Hajira Jabeen^a

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 377-390, 2018, DOI:10.1080/10798587.2017.1295678

Abstract This paper presents a novel random controlled pool base differential evolution algorithm (RCPDE) where powerful mutation strategy and control parameter pools have been used. The mutation strategy pool contains mutations strategies having diverse parameter values, whereas the control parameter pool contains varying nature pairs of control parameter values. It has also been observed that with the addition of rarely used control parameter values in these pools are highly beneficial to enhance the performance of the DE algorithm. The proposed mutation strategy and control parameter pools improve the solution quality and the convergence speed of DE algorithm. The simulation results of… More >

Shuangsheng Zhang^1,5, Hanhu Liu¹, Jing Qiang^2,*, Hongze Gao^3,*, Diego Galar⁴, Jing Lin⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 373-394, 2019, DOI:10.32604/cmes.2019.03825

Abstract Coupling Bayes’ Theorem with a two-dimensional (2D) groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity (M ), release location ( X₀ , Y₀) and release time (T₀), based on monitoring well data. To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters, a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy. To demonstrate how the model works, an exemplar problem with an instantaneous release of a contaminant in a confined groundwater… More >