Genetic Programming Applications in Chemical Sciences and Engineering

Vyas, Renu; Goel, Purva; Tambe, Sanjeev S.

doi:10.1007/978-3-319-20883-1_5

Cited by 18 publications

(8 citation statements)

References 118 publications

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“…There have been many reports in recent years exploiting the potential of GP for system identification, particularly in chemical engineering applications [20]. In order to assess the suitability of GP for nonlinear dynamic system identification, one-step-head (OSA) prediction of a well-known benchmark chemical pro-cess, the Eaton-Rawlings reactor model, was investigated.…”

Section: Identification Of the Eaton-rawlings Reactormentioning

confidence: 99%

Model predictive control of non-domestic heating using genetic programming dynamic models

Dou

Lopes

Rockett

et al. 2020

Applied Soft Computing

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Section: Identification Of the Eaton-rawlings Reactormentioning

confidence: 99%

Model predictive control of non-domestic heating using genetic programming dynamic models

Dou

Lopes

Rockett

et al. 2020

Applied Soft Computing

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“…Among these methods, the GEP technique is an efcient approach that exhibits the capability to estimate complex as well as highly nonlinear problems [2,54,55]. Moreover, there exist certain drawbacks to applying the GEP, including the fact that (a) it is computationally complex, (b) the generated solutions may be overft, and (c) the GEP modelling requires additional complex heuristics to achieve optimal results [56]. Terefore, multiexpression programming (MEP) has been applied here alongside the GEP approach to compute the P s -ES in the current study to compare the two genetic programming approaches.…”

Section: Introductionmentioning

confidence: 99%

Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling

Jalal

Iqbal

Khan

et al. 2023

Advances in Materials Science and Engineering

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In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit w L , plastic limit w P , plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content w o p t ) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing.

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“…When the stopping condition (the maximum number of generations or a satisfactory solution) is reached, the whole process is finished [ 66 ]. If the termination conditions for achieving the optimum iteration or the favorite fitness value are not satisfied, then the Roulette wheel procedure is used, which chooses the viable chromosomes of the first generation and moves them on to the next generation [ 67 ]. This method will be repeated for a certain number of generations or before the right solution is found [ 68 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

Khan

Zafar

et al. 2021

Materials

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The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.

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Genetic Programming Applications in Chemical Sciences and Engineering

Cited by 18 publications

References 118 publications

Model predictive control of non-domestic heating using genetic programming dynamic models

Model predictive control of non-domestic heating using genetic programming dynamic models

Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling

Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

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