A coupled and interactive influence of operational parameters for optimizing power output of cleaner energy production systems under uncertain conditions

Abstract

Summary The mechanisms in proton‐exchange membrane fuel cells (PEMFCs) cannot be explicitly represented by a mathematical function because the PEMFC system is multi‐dimensional and complex and represents uncertainty in operation variables, which cannot be modeled by experiments or by trial‐and‐error approach. Therefore, this work proposes to study the coupled and interactive influence of stack current (SC), stack temperature (ST), oxygen excess ratio (OER), hydrogen excess ratio (HER), and inlet air humidity (IAH) for optimizing the power output of PEMFC. The data obtained from the experiments have been inserted into architecture of automated neural‐network search, which automates the selection of error function, activation function, uncertainties in inputs and number of hidden neurons in formulation of a robust and accurate model for power density as a function of five operational variables. Among the operational variables, the correlation coefficient between the SC and the output power is the highest, followed by OER, and the ST. However, for HER and IAH, the power output follows negative nonlinear relation. The optimization converged at 130th iteration results in maximum power output of 3410 W for an optimum value of SC (51A), ST (59°C), OER (3:2), HER (1:10), and IAH (0.8).

Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining

“…The above established guidelines were applied to validate the optimal node numbers in several publications [15,19,20,[22][23][24], which fall under the topics of temperature, mechanical behavior, and material development. The calculations of the node number range of the hidden layer for these publications were performed with PF values of 10 and 70, respectively.…”

“…It shows that the optimal values in five publications are in the range defined by Equations (11)- (13), except that the optimal node number in literature [24] is slightly different to the calculated range. Four [19,20,22,23] of them are in the range with an assumption PF equal to 10 and one [15] in the range with PF equal to 70. The possible total numbers of trials are also given in Table 4, and fewer trials are needed, compared with the trial and error method [15] and some empirical equations [20,22].…”

“…Four [19,20,22,23] of them are in the range with an assumption PF equal to 10 and one [15] in the range with PF equal to 70. The possible total numbers of trials are also given in Table 4, and fewer trials are needed, compared with the trial and error method [15] and some empirical equations [20,22]. The relation between A and PF was fitted by an exponential equation (Equation (13)) as shown in Figure 4.…”

“…Table 1 lists the publications on the optimization of the single hidden layer node number. To seek an optimal one, many applied the trial and error method in a diverse range of the node numbers [13][14][15][16][17]; whilst others merely followed the rules of thumb proposed in the literature [18][19][20][21][22][23][24], and the application of rules can significantly reduce the number of trials. Six selected rules of thumb [18][19][20][21][22][23][24][25] used in the respective publication are given below and indicated in Table 1 with an asterisk.…”

A Low‐Carbon Design Method Integrating Structure Design and Injection Process Design for Injection Molding Machines

“…An adaptive promising subpopulation sorting-based environmental selection strategy is provided for problems which may have irregular Pareto fronts. Chen et al [24] proposed an automated neural network search method in optimizing the power output of cleaner energy production systems.…”