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

Rabiatul Adawiyah Ali; Nik Nor Liyana Nik Ibrahim; Wan AzlinaWan Abdul Karim Ghani; Nor Samsiah Sani; Hon Loong Lam

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

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

Chemical Engineering Environmental Engineering

Rabiatul Adawiyah Ali¹, Nik Nor Liyana Nik Ibrahim This email address is being protected from spambots. You need JavaScript enabled to view it.¹, Wan AzlinaWan Abdul Karim Ghani¹, Nor Samsiah Sani², and Hon Loong Lam³

¹Department of Chemical and Environmental Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
²Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
³Department of Chemical and Environmental Engineering, The University of Nottingham Malaysia Campus, Semenyih, Selangor 43500, Malaysia

Received: December 31, 2021
Accepted: April 19, 2022
Publication Date: May 20, 2022

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202302_26(2).0012

ABSTRACT

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, time consuming 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.

Keywords: P-graph, WEKA, optimisation tool, data analytics, decision tool

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