A Hybrid P-Graph And WEKA Approach In Decision-Making: Waste Conversion Technologies Selection

Journal of Applied Science and Engineering

Nor Samsiah Sani¹  Hon Loong Lam²  Nik Nor Liyana Nik Ibrahim³  Rabiatul Adawiyah Ali³  Wan AzlinaWan Abdul Karim Ghani³ 

[1] Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia;Department of Chemical and Environmental Engineering, The University of Nottingham Malaysia Campus, Semenyih, Selangor 43500, Malaysia;Department of Chemical and Environmental Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia;

关键词: p-graph;    weka;    optimisation tool;    data analytics;    decision tool;   

DOI  :  10.6180/jase.202302_26(2).0012

来源: DOAJ

【 摘 要 】

Process system engineering approaches have a considerably broader reach, which is one of the benefits for decision-makers. Making a decision, however, has many drawbacks. It includes biased decisions, timeconsuming analyses, and an unpredictable future. A decision-making integration framework based on hybrid process network synthesis and machine learning was presented in this study. The municipal solid waste management case study uses to demonstrate the applicability decision-making framework. The focus of this paper is to facilitate equipment selection for municipal solid waste management. P-graph was used to generate the 160 possible structures. Then, using the WEKA software, the data from the feasible structure would be processed and evaluated using the chosen algorithm. The J48 is the best model for equipment selection using an 80:20 ratio train and test learning technique in WEKA. The kappa statistics J48 algorithm function for the training and testing dataset is 0.9722 and 1. The mean absolute error and root mean square error are 0.0042 and 0.0354. The decision-making integration framework represents by a graphical user interface in MATLAB. The focus of user interface for selection of waste conversion technologies. As a result, the model can be used to determine the best municipal solid waste conversion technology.

【 授权许可】

Unknown