The optimal operation of fuel cells in changing environmental and variable load conditions requires mathematical modeling. The electrochemical behavior of polymer electrolyte membrane fuel cells (PEMFC) is commonly described with a semi‐empirical model requiring fuel cell specific model parameter values. A large number of different nature inspired, heuristic optimization methods have been proposed for this PEMFC parameter estimation problem. In this study, those studies are listed and critically reviewed. In particular, the aim is to elaborate the generalization ability of the results and discuss the fair comparison of the algorithms used for the parameter estimation of the polarization curve. The observations made in this review could further increase the quality of future contributions in this particular area, as well as applications of heuristic optimization methods in other related problems in fuel cell systems.

Furfural, acetic acid, and sulfates are found in the hemicellulose (HMC) fraction of lignocellulosic biomass. Separation of furfural, acetic acid, and sulfates from monosaccharides by four nanofiltration (NF) membranes was evaluated with a model solution of glucose, xylose, furfural, acetic acid, and sulfates. Results showed that Alfa Laval NF99HF is the most promising membrane to purify monosaccharides, with the retentions of xylose (85%), glucose (95%), and with the minimum sulfate retention. pH has the highest impact on the retention of all solutes and there is no significant effect of temperature on the retentions of sulphates and acetic acid. Lower pH and temperature are favored to maximize the monosaccharide retention and to remove acetic acid while retaining more furfural with the monosaccharides. Moreover, fouling tendency is maximized at lower pH and higher temperatures. According to the statistical analysis, the retentions of glucose, xylose, furfural, sulfates, and acetic acid are 95%, 90%, 20%, 88%, and 0%, respectively at pH 3 and 25 °C. The presence of sulfates favors the separation of acetic acid and furfural from monosaccharides.

The applications of evolutionary optimizers such as genetic algorithms, differential evolution, and various swarm optimizers to the parameter estimation of the fuel cell polarization curve models have increased. This study takes a novel approach on utilizing evolutionary optimization in fuel cell modeling. Model structure identification is performed with genetic algorithms in order to determine an optimized representation of a polarization curve model with linear model parameters. The optimization is repeated with a different set of input variables and varying model complexity. The resulted model can successfully be generalized for different fuel cells and varying operating conditions, and therefore be readily applicable to fuel cell system simulations.

Cross direction (CD) control in sheet-forming process forms a challenging problem with high dimensions. Accounting the interactions between different properties and actuators, the dimensionality increases further and also computational issues arise. We present a multiple property controller feasible to be used especially with imaging measurements that provide high sampling frequency and therefore enable short control interval. The simulation results state the benefits of multiple property CD control over single property control and single property control using full feedforward compensation. The controller presented may also be tuned in automated manner and the results demonstrate the effect of tuning on input saturation

An advanced model-based control method for the integrated fuel processing and a fuel cell system consisting of ethanol reforming, hydrogen purification, and a proton exchange membrane fuel cell is presented. For process identification, a physical model of the process chain was constructed. Subsequently, the simulated process was approximated with data-driven control models. Based on these control models, a hierarchical control framework consisting of model predictive controller and a global optimization algorithm was introduced. The performance of the new control method was evaluated with simulations. Results indicate that the new optimization concept enables resource efficient and fast control of the studied energy conversion process. Fast and efficient fuel cell process could then provide sustainable power source for autonomous and mobile applications in the future.

Increasing demand for critical raw materials and energy transition metals sets new targets for the mineral processing, also resulting as higher requirements for the simulation tools during process design and optimization. This study presents a framework for global uncertainty evaluation of modelled plant-wide processes, where the propagation of uncertainty sources is addressed. The uncertainties exist, for example in operational and design parameters and in material properties. The approach was demonstrated with a typical mineral processing flowsheet simulated with commercial software. First, domain knowledge was adopted to screen the parameter space and then Monte Carlo simulation was performed. After this, the generated data set was used to identify surrogate models between the uncertain inputs and process performance indicators. Finally, a global sensitivity analysis was conducted to identify the effects of uncertainties to the decision-making in process design. The results were particularly used to locate the process points where accurate information is needed for the robust process design, or where on-line measurements would be preferred to establish on-line optimization.