A decision-making system for the entire life cycle industry chain based on data mining technology optimization

Bahar Asgarova, Elvin Jafarov, Nicat Babayev, Allahshukur Ahmadzada, Vugar Abdullayev and Yitong Niu

Data and Metadata, 2024, vol. 3, .381

Abstract: When developing a biomass production plan, the factors that influence decision makers include not only the different parts of the biomass supply chain itself, but also the social, environmental and economic impacts of the biomass system and the degree of difficulty in developing it within a particular country. In order to take these factors into account, this paper proposes a two-tier generalised decision-making system (gBEDS) for biomass, with a database at its core, including basic biomass information and detailed decision-making information, in addition to a database of scenarios and a library of case studies that provide demonstrations for new users. On the basis of the database, the decision-making system includes a simulation module for the unit process (uP) and a genetic algorithm for optimising the decisions. With the help of a graphical interface, users can define their own biomass supply chain and evaluate it environmentally, economically, socially or otherwise; on the basis of a simulation and optimisation model of the whole life cycle of biomass production, the system uses data mining methods (fuzzy c-mean clustering and decision trees) to determine the optimal geographic location of the biomass raw material collection and storage and conversion plants. Madab was used to develop a computational model for biomass planning parameters (e.g. costs and c02 emissions) for the biomass supply chain. At the same time, a visual representation of the bioenergy conversion plant and storage data is made using Geographic Information Systems (GIs) to support users in making decisions based on intelligent outputs. Thus, gBEDS supports biomass national planners in developing an effective biomass production plan with comprehensive evaluation, and local designers and implementers in defining optimised, detailed unit processes to implement said plan.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:.381:id:1056294dm2024381

DOI: 10.56294/dm2024.381

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