The Optimization of Energy Consumption and CO2 Emission in the Product Hazardous Substances Report Making

Chao Chung Hsu*, Chun Cheng Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years, products that are free of hazardous substances and 2050 Net zero are the focus of environmental sustainability issues, and the standards formulated by various countries (e.g., RoHS, ISO14064-1) are mandatory requirements that brands cannot ignore. When products are to be imported, they must present relevant reports (i.e., product hazardous substance reports, greenhouse gas reports) and pass customs inspection before they can be sold in the country. The key to complying with the standards is to use raw materials without hazardous substances and reduce electricity use during the production process. However, previous works only focused on production development technology, but ignored the issue of energy consumption. Therefore, this study proposes the product hazardous substance report making energy consumption problem (PHSRMECP), which has the goal of low carbon emissions and a new matching method to solve it. As the complexity and solution difficulty of PHSRMECP are NP-Hard, this study proposes a heuristic algorithm to solve it. First, the Analytic Hierarchy Process (AHP), which is commonly used in multi-objective decision-making, is used to match reports and engineers based on weights, and then, the divide and conquer genetic algorithm (DnCGA) is applied to identify the best match. This new heuristic algorithm is based on the genetic algorithm, which is mixed with the divide-and-conquer simplified algorithm, in order to consider the speed and quality of the solution. The research goal is achieved by minimizing the energy consumption required to collect and compile reports. The verification method is applied to simulate the real data, and the results show that the proposed method is more effective than the original manual matching method: reduced 73,741.9 KgCO2e emissions, and verified small, medium, and large-scale data statistics to effectively reduce CO2 emissions by 15.3% to 29.2%.

Original languageEnglish
Pages (from-to)889-907
Number of pages19
JournalInternational Journal of Precision Engineering and Manufacturing - Green Technology
Volume11
Issue number3
DOIs
StatePublished - May 2024

Keywords

  • Analytic hierarchy process
  • CO emissions
  • Divide and conquer genetic algorithm
  • Energy consumption
  • Product hazardous substance report

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