Department of Chemical Engineering, GH Patel College of Engineering and Technology, Vallabh Vidya Nagar 388 120, Gujarat, India.
Bioresour Technol. 2011 Sep;102(18):8569-81. doi: 10.1016/j.biortech.2011.03.108. Epub 2011 Apr 9.
Biohydrogen is a sustainable energy resource due to its potentially higher efficiency of conversion to usable power, non-polluting nature and high energy density. The purpose of modeling and optimization is to improve, analyze and predict biohydrogen production. Biohydrogen production depends on a number of variables, including pH, temperature, substrate concentration and nutrient availability, among others. Mathematical modeling of several distinct processes such as kinetics of microbial growth and products formation, steady state behavior of organic substrate along with its utilization and inhibition have been presented. Present paper summarizes the experimental design methods used to investigate effects of various factors on fermentative hydrogen production, including one-factor-at-a-time design, full factorial and fractional factorial designs. Each design method is briefly outlined, followed by the introduction of its analysis. In addition, the applications of artificial neural network, genetic algorithm, principal component analysis and optimization process using desirability function have also been highlighted.
生物氢由于其潜在的更高的可用功率转换效率、无污染性质和高能量密度，是一种可持续的能源资源。建模和优化的目的是改善、分析和预测生物制氢。生物制氢取决于许多变量，包括 pH 值、温度、底物浓度和营养物质的可用性等。已经提出了对几种不同过程的数学建模，例如微生物生长和产物形成的动力学、有机底物的稳态行为以及其利用和抑制。本文总结了用于研究各种因素对发酵产氢影响的实验设计方法，包括单因素设计、完全因子设计和部分因子设计。简要概述了每种设计方法，然后介绍了其分析方法。此外，还强调了人工神经网络、遗传算法、主成分分析和使用理想性函数的优化过程的应用。
