Introduction to stochastic programming

Ionic liquids (ILs) offer a wide range of promising applications because of their much enhanced properties. However, further development of such materials depends on the fundamental understanding of their hierarchical structures and behaviors, which requires multiscale strategies to provide coupling among various length scales. In this review, we first introduce the structures and properties of these typical ILs. Then, we introduce the multiscale modeling methods that have been applied to the ILs, covering from molecular scale (QM/MM), to mesoscale (CG, DPD), to macroscale (CFD for unit scale and thermodynamics COSMO-RS model and environmental assessment GD method for process scale). In the following section, we discuss in some detail their applications to the four scales of ILs, including molecular scale structures, mesoscale aggregates and dynamics, and unit scale reactor design and process design and optimization of typical IL applications. Finally, we address the concluding remarks of multiscale strat...

Multiscale Studies on Ionic Liquids

Advances in surrogate based modeling, feasibility analysis, and optimization: A review

Metamodeling is becoming a rather popular means to approximate the expensive simulations in today’s complex engineering design problems since accurate metamodels can bring in a lot of benefits. The metamodel accuracy, however, heavily depends on the locations of the observed points. Adaptive sampling, as its name suggests, places more points in regions of interest by learning the information from previous data and metamodels. Consequently, compared to traditional space-filling sampling approaches, adaptive sampling has great potential to build more accurate metamodels with fewer points (simulations), thereby gaining increasing attention and interest by both practitioners and academicians in various fields. Noticing that there is a lack of reviews on adaptive sampling for global metamodeling in the literature, which is needed, this article categorizes, reviews, and analyzes the state-of-the-art single−/multi-response adaptive sampling approaches for global metamodeling in support of simulation-based engineering design. In addition, we also review and discuss some important issues that affect the success of an adaptive sampling approach as well as providing brief remarks on adaptive sampling for other purposes. Last, challenges and future research directions are provided and discussed.

A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design

Machine learning technology in biodiesel research: A review

Adaptive sequential sampling for surrogate model generation with artificial neural networks

Process synthesis of biodiesel production plant using artificial neural networks as the surrogate models

A perspective on process synthesis: Challenges and prospects

Aqueous amine is a solvent considered for carbon dioxide (CO2) recovery from the flue gas of a refinery gas turbine by chemical absorption/desorption process. The performance and the economics of this process depend on the choice of the amine absorbent, the concentration of the amine absorbent, the number of stages in the absorber and stripper columns, and the operating conditions. We used response surface methodology (RSM)—a simulation–optimization technique, which uses local searches to estimate an appropriate direction to reduce the objective function—to optimize the amine-based CO2 capture process in a previous work [Nuchitprasittichai and Cremaschi Comput. Chem. Eng. 2011, 35, 1521–1531]. However, RSM does not provide any information about the quality of the obtained solution. In this paper, the RSM results are compared to those obtained by optimizing a global surrogate model of the system over the whole decision space with a global solver. We used an artificial neural network (ANN) as the global sur...

Optimization of CO2 Capture Process with Aqueous Amines—A Comparison of Two Simulation–Optimization Approaches

This study compares eight surrogate-model construction approaches using computational experiments. The construction approaches considered include: Artificial Neural Networks (ANNs), Automated Learning of Algebraic Models using Optimization (ALAMO), Radial Basis Networks (RBNs), Extreme Learning Machines (ELMs), Gaussian Progress Regression (GPR), Random Forests (RFs), Support Vector Regression (SVR), and Multivariate Adaptive Regression Splines. Each approach is used to construct surrogate models for predicting the outputs of thirty-four test functions. The data used to construct surrogate models were generated using Latin Hypercube Sampling (LHS), Halton and Sobol sampling methods. We studied the impact of sampling method, sample size, and challenge function characteristics on the accuracy of surrogate model predictions. Our results revealed that for smaller sample sizes, on average, surrogate models trained using the data points generated by Sobol sequences provided the best estimation for all surrogate model construction approaches. As the sample size increased, the impact of sampling methods diminished. The comparison of surrogate-modeling construction approaches at large sample sizes revealed that surrogate models trained using ANN, ALAMO, and ELM yielded smaller root mean squared error and higher adjusted R-squared values than the models trained using the rest of the approaches.

Efficient Surrogate Model Development: Impact of Sample Size and Underlying Model Dimensions

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