Development of a novel multi-criteria decision method for determining the best available techniques in cleaner production applications

In this doctoral dissertation, a multi-criteria decision-making (MCDM) model was developed to support the prioritization of Best Available Techniques (BAT/MET) in order to contribute to the advancement of cleaner production practices in industrial sectors. To enable the practical implementation of the developed model, a web-based software named "Clean Decision Software" was designed. The study was specifically conducted with a focus on the textile sector. Based on real field data, the BAT prioritization process was systematically evaluated using the Clean Decision Software. The research initially explored the fundamental principles of the cleaner production approach, environmental policy frameworks, and the BAT concept in detail. In this context, 52 evaluation criteria that could be used in BAT selection were identified through literature review and expert opinions. Subsequently, 29 commonly used MCDM methods from the literature were examined, and 22 of them were included in the evaluation based on their suitability. The application of these 22 methods with the 52 evaluation criteria revealed major challenges such as increased evaluation time and information complexity. The large number of criteria made accurate and consistent scoring difficult and also affected the performance of the methods. Therefore, considering the application results, the scope was narrowed down, and 10 evaluation criteria with high significance, measurability, and comparability were determined. These evaluation criteria were defined, their scoring scales were established, and finalized. At this point, a multi-criteria decision model specific to cleaner production was developed based on the obtained data. This model consists of five steps: (i) Scoring BAT alternatives on a 1–5 scale in the decision matrix, (ii) Weighting the criteria using the Full Consistency Method (FUCOM), (iii) Ranking alternatives using four selected MCDM methods Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), COmbinative Distance-based ASsessment (CODAS), and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), (iv) Integrating the rankings using the Copeland method, and (v) Conducting a sensitivity analysis to test the model's reliability. This structure ensures both diversity in rankings and robustness in decision-making, establishing an objective basis for BAT prioritization. To validate the model and test the functionality of the software, a field study was conducted in an integrated textile facility. Process flow diagrams for production and auxiliary processes were prepared, and three years' worth of data on energy, water, chemical, fuel, and raw material consumption, as well as wastewater, air emissions, and solid waste generation, were collected. Based on this data, specific (per unit product) values were calculated and compared with Integrated Pollution Prevention and Control (IPPC–96/61/EC) BAT References (BREF) documents and literature data. Potential reduction rates were estimated as 26% in water consumption, 18% in energy consumption, 21% in chemical usage, 16% in solid waste generation, 14% in air emissions, and 11% in noise levels. As a result of the cleaner production assessment, a total of 321 BATs specific to the textile sector were identified under eight main categories: raw material management, water-wastewater management, energy efficiency, chemical management, air emissions, solid waste, noise management, and general preventive measures. The developed decision model was transformed into a user-friendly web-based software titled Clean Decision Software. The software allows users to define evaluation criteria and BAT alternatives, conduct analyses using different MCDM methods, and view tabular comparisons. It provides a dynamic decision support system that enables decision-makers to use multiple methods comparatively, analyze the results, and select the most appropriate BATs objectively based on facility-specific conditions. To validate the model, a detailed case study was carried out in an integrated textile facility. The BATs weighted by facility personnel were scored in the Clean Decision Software, ranked using TOPSIS, VIKOR, CODAS, and MARCOS, and final rankings were obtained through the Copeland method. Additionally, a sensitivity analysis was conducted to assess the robustness of the decision model, revealing the impact of changing weights on alternative rankings. The results showed that BATs which are cost-effective, easily implementable, and support information-based control were prioritized across multiple methods. Specifically, BATs such as "preparing annual waste and emission inventory reports based on mass balances," "measuring machine-based water consumption," "installing chemical dosing systems," and "integrating energy monitoring systems" consistently ranked among the top across all methods. This doctoral study, as a first in the literature, developed a user-friendly decision support system that integrates a multi-criteria and multi-method structure, tested with real field data, in contrast to BAT prioritization approaches that are generally conducted with a single method and a limited number of criteria. The software enhances transparency in decision-making processes, allows for the analysis of different scenarios, and serves as an effective tool for industrial enterprises aiming to improve environmental performance and resource efficiency. Ultimately, the Clean Decision Software was evaluated as a flexible, scalable, and highly applicable tool not only in academia but also in industrial settings. Future work may involve expanding the tool to cover BAT lists for other industrial sectors, integrating with decision support techniques such as fuzzy logic or life cycle analysis, and supporting implementation with monitoring modules.