An introduction to heat transfer

Thank you for downloading analysis synthesis and design of chemical processes. Maybe you have knowledge that, people have search numerous times for their chosen readings like this analysis synthesis and design of chemical processes, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful virus inside their desktop computer.

Analysis Synthesis And Design Of Chemical Processes

Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

This research’s genesis is in two aspects: first, a guaranteed solution for mitigating the grey wolf optimizer’s (GWO) defect and deficiencies. Second, we provide new open-minding insights and deep views about metaheuristic algorithms. The population-based GWO has been recognized as a popular option for realizing optimal solutions. Despite the popularity, the GWO has structural defects and uncertain performance and has certain limitations when dealing with complex problems such as multimodality and hybrid functions. This paper tries to overhaul the shortcomings of the original process and develops a GWO variant enhanced with a covariance matrix adaptation evolution strategy (CMAES), levy flight mechanism, and orthogonal learning (OL) strategy named GWOCMALOL. The algorithm uses the levy flight mechanism, orthogonal learning strategy, and CMAES to bring more effective exploratory inclinations. We conduct numerical experiments based on various functions in IEEE CEC2014. It is also compared with 10 other algorithms with competitive performances, 7 improved GWO variants, and 11 advanced algorithms. Moreover, for more systematic data analysis, Wilcoxon signed-rank test is used to evaluate the results further. Experimental results show that the GWOCMALOL algorithm is superior to other algorithms in terms of convergence speed and accuracy. The proposed GWO-based version is discretized into a binary tool through the transformation function. We evaluate the performance of the new feature selection method based on 24 UCI data sets.​ Experimental results show that the developed algorithm performs better than the original technique, and the defects are resolved. Besides, we could reach higher classification accuracy and fewer feature selections than other optimization algorithms. A narrative web service at http://aliasgharheidari.com will offer the required data and material about this work.

Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection

Whale Optimization Algorithm (WOA) is a popular swarm-based algorithm with some spotted defects in its generated patterns during the searching phases. In this study, an enhanced WOA-based method is proposed in order to overcome the drawbacks of slow convergence speed and easy falling of WOA into the local optimum. The designed variant is called enhanced WOA (EWOA), which combines two strategies at the same time. First, a new communication mechanism (CM) is embedded into the basic WOA to promote the global optimal search ability and the exploitation tendency of the WOA. Then, the Biogeography-based Optimization (BBO) algorithm is partially utilized to harmonize the exploration and exploitation trends. A representative set of comprehensive benchmark cases and three engineering cases are utilized to verify the advantages of the proposed EWOA. The experimental results show that the exploration ability, exploitation ability, state of the balance, and convergence style of the algorithm has been improved significantly. Based on results, the proposed EWOA is a promising and excellent algorithm, and it has achieved better solution quality and faster convergence rate compared with other most advanced algorithms. For access to material and guide for users of this paper, we host an online page at https://aliasgharheidari.com .

Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance

https://www.econstor.eu/bitstream/10419/243697/1/1683642686.pdf

Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm

https://www.econstor.eu/bitstream/10419/244055/1/1694119025.pdf

An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm

Optimal scheduling of electric vehicles aggregator under market price uncertainty using robust optimization technique

https://www.econstor.eu/bitstream/10419/243692/1/1683638492.pdf

Yan Cao

Papers

Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm

An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm

Optimal scheduling of electric vehicles aggregator under market price uncertainty using robust optimization technique

Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics

Optimal operation of CCHP and renewable generation-based energy hub considering environmental perspective: An epsilon constraint and fuzzy methods