An Innovative Genetic Algorithms-Based Inexact Non-Linear Programming Problem Solving Method

In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.

Learning from a carbon dioxide capture system dataset: Application of the piecewise neural network algorithm

This paper presents the application of a neural network rule extraction algorithm,called the piecewise linear artificial neural network or PWL-ANN algorithm,on a carbon capture process system dataset.The objective of the application is to enhance understanding of the intricate relationships among the key process parameters.The algorithm extracts rules in the form of multiple linear regression equations by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network(ANN).The PWL-ANN algorithm overcomes the weaknesses of the statistical regression approach,in which accuracies of the generated predictive models are often not satisfactory,and the opaqueness of the ANN models.The results show that the generated PWL-ANN models have accuracies that are as high as the originally trained ANN models of the four datasets of the carbon capture process system.An analysis of the extracted rules and the magnitude of the coefficients in the equations revealed that the three most significant parameters of the CO_(2) production rate are the steam flow rate through reboiler,reboiler pressure,and the CO_(2) concentration in the flue gas.

Recent progress and new developments of applications of artificial intelligence (AI), knowledge-based systems (KBS), and Machine Learning (ML) in the petroleum industry

Over the past two decades,broad-based and intense efforts have been devoted to apply concepts and methodologies from information technology(IT)and artificial intelligence(AI)to informatics research related to energy and environmental systems.Both academic research and industrial practices have generated an impressive amount of literature,which spans virtually every aspect of synergistic work among these disciplines.Informatics and systems analysis techniques are now being widely used by the practicing engineers and scientists to solve a broad range of problems in the petroleum industry.Speaking at the World Economic Forum at Davos Switzerland in 2017,Ginni Rometty,IBM′CEO at that time(now Executive Chairperson of IBM),described AI's role in the partnership between humans and machines as“augmented cognition.”In other words,AI not only supports but augments human cognition so that humans can be more efficient and“do a better job”[1,2].This should also be true and very beneficial for the petroleum industry.

Towards explicit representation of an artificial neural network model: Comparison of two artificial neural network rule extraction approaches

In the quest for interpretable models,two versions of a neural network rule extraction algorithm were proposed and compared.The two algorithms are called the Piece-Wise Linear Artificial Neural Network(PWL-ANN)and enhanced Piece-Wise Linear Artificial Neural Network(enhanced PWL-ANN)algorithms.The PWL-ANN algorithm is a decomposition artificial neural network(ANN)rule extraction algorithm,and the enhanced PWL-ANN algorithm improves upon the PWL-ANN algorithm and extracts multiple linear regression equations from a trained ANN model by approximating the hidden sigmoid activation functions using N-piece linear equations.In doing so,the algorithm provides interpretable models from the originally trained opaque ANN models.A detailed application case study illustrates how the generated enhanced-PWL-ANN models can provide understandable IF-THEN rules about a problem domain.Comparison of the results generated by the two versions of the PWL-ANN algorithm showed that in comparison to the PWL-ANN models,the enhanced-PWL-ANN models support improved fidelities to the originally trained ANN models.The results also showed that more concise rule sets could be generated using the enhanced-PWL-ANN algorithm.If a more simplified set of rules is desired,the enhanced-PWL-ANN algorithm can be combined with the decision tree approach.Potential application of the algorithms to domains related to petroleum engineering can help enhance understanding of the problems.