Selection of Best Available Techniques for Water Softening/Ion Exchange Processes Employing TOPSIS Decision Making Model

Öz

There is a need for different qualities of process water in the manufacturing industry, depending on the purpose of use, the process and its effects on the product. The most widely used process water preparation system is column systems using cationic ion exchange resins for hardness removal. Various best available techniques (BAT) can be applied to ion exchangers to reduce process water production costs and environmental impacts. In this study, it was aimed to evaluate the BATs presented in order to increase the environmental performance of cationic ion exchange resin systems and to determine the most appropriate techniques using the multi-criteria decision making method (MCDM). In this context, BATs were prioritized according to 11 different evaluation criteria using the Ranking Preference Technique by Similarity to Ideal Solution (TOPSIS). As a result, optimizing regeneration durations and frequencies using online hardness sensors was identified as the top priority BAT for cationic ion exchange resin systems.

Anahtar Kelimeler

• Best available techniques (BAT)
• Ion exchange
• Resin
• TOPSIS
• Water softening

İyon Değiştirme Prosesi Kullanan Su Yumuşatma Sistemlerinde TOPSIS Karar Verme Modeliyle Mevcut En İyi Tekniklerin Seçimi

Öz

İmalat sanayinde kullanım amacına, prosese ve ürün üzerindeki etkilerine bağlı olarak farklı kalitelerde proses suyu gereksinimi bulunmaktadır. En yaygın kullanılan proses suyu hazırlama sistemi sertlik giderimi amacıyla katyonik iyon değiştirme reçineleri kullanan kolon sistemlerdir. İyon değiştiricilerde, proses suyu üretim maliyetlerini ve çevresel etkileri azaltmak amacıyla çeşitli mevcut en iyi teknikler (MET) uygulanabilmektedir. Bu çalışmada, katyonik iyon değiştirme reçine sistemlerin çevresel performanslarını arttırabilmek amacıyla sunulan MET’lerin değerlendirilmesi ve çok ölçütlü karar verme metodu (ÇÖKVM) kullanılarak en uygun tekniklerin belirlenmesi amaçlanmıştır. Bu kapsamda MET’ler, İdeal Çözüme Benzerlik Yoluyla Sıralama Tercihi Tekniği (TOPSIS) kullanılarak 11 farklı değerlendirme kriterine göre önceliklendirilmiştir. Sonuç olarak eş-zamanlı sertlik sensörleri kullanımıyla rejenerasyon süreleri ve sıklıklarının optimize edilmesi katyonik iyon değiştirme reçine sistemleri için en öncelikli MET olarak belirlenmiştir.

Anahtar Kelimeler

• İyon değiştirme
• Mevcut en iyi teknikler (MET)
• Reçine
• Su yumuşatma
• TOPSIS

Kaynakça

1. [1] Türkiye İstatistik Enstitüsü (TÜİK), 2018. Çevre Enerji İstatistikleri. http://www.tuik.gov.tr (Erişim Tarihi: 17.04.21).
2. [2] Dabska, A. 2019. Hydraulic Conductivity of Compacted Lime-Softening Sludge Used as Landfill Liners. Water Air Soil Pollution, 230: 280.
3. [3] Gitisa, V., Hankinsb, N. 2018. Water Treatment Chemicals: Trends and Challenges. Journal of Water Process Engineering, 25, 34–38.
4. [4] Shahmirzadi, M.A.A., Hosseini, S.S., Luo, J., Ortiz, I. 2018. Significance, Evolution and Recent Advances in Adsorption Technology, Materials and Processes for Desalination, Water Softening and Salt Removal. Journal of Environmental Management, 215, 324-344.
5. [5] Micari, M., Moser, M., Cipollina, A., Tamburini, A., Micale, G., Bertsch, V. 2020. Towards the Implementation of Circular Economy in The Water Softening İndustry: A Technical, Economic and Environmental Analysis. Journal of Cleaner Production, 255, 120291.
6. [6] Aragaw, T.A., Ayalew, A.A. 2019. Removal of Water Hardness Using Zeolite Synthesized from Ethiopian Kaolin by Hydrothermal Method. Water Practice a Technology, 14 (1), 145-159.
7. [7] Vajnhandl, S., Valh, J.V. 2014. The Status of Water Reuse in European Textile Sector. Journal of Environment Management, 141, 29-35.
8. [8] Mughees, W., Al-Ahmad, M. 2015. Application of Water Pinch Technology in Minimization of Water Consumption at a Refinery. Computers & Chemical Engineering, 73, 34-42.

9. [9] Türkiye Bilimsel Ve Teknolojik Araştırma Kurumu (TÜBİTAK), 2016. Sanayide Temiz Üretim Olanaklarının ve Uygulanabilirliğinin Belirlenmesi Projesi. Maya Sektöründe Temiz Üretim El Kitabı, Kocaeli.
10. [10] Dursun, M. 2015. An Integrated Approach for the Evaluation of Wastewater Treatment Alternatives. Proceedings of the World Congress on Engineering and Computer Science (WCECS), October 21–23.
11. [11] Simsek, E., Demirel, Y. E., Ozturk, E., Kitis, M. 2021. Use of Multi-Criteria Decision Models for Optimization of Selecting The Most Appropriate Best Available Techniques in Cleaner Production Applications: A Case Study in A Textile Industry. Journal of Cleaner Production, 130311.
12. [12] Ozturk, E. 2018. Applying Analytical Decision Methods for Determination of The Best Treatment Alternative to Remove Emerging Micropollutants from Drinking Water and Wastewater: Triclosan Example. Environmental Science and Pollution Research, 25, 30517-30546.
13. [13] Pazand, K., Hezarkhani, A., and Ataei, M. 2012. Using TOPSIS Approaches for Predictive Porphyry Cu Potential Mapping: A Case Study in Ahar-Arasbaran Area (NW, Iran). Computers & Geosciences, 49, 62-71.
14. [14] Chu, J., and Su, Y. 2012. The Application of TOPSIS Method in Selecting Fixed Seismic Shelter for Evacuation in Cities. Systems Engineering Procedia, 3, 391-397.
15. [15] Srdjevic, B., Medeiros, Y.D.P., Faria, A.S. 2004. An Objective Multi-Criteria Evaluation of Water Management Scenarios. Water Resources Management, 18, 35–54.
16. [16] Kwong, C.K., Tam, S.M. 2002. Case-Based Reasoning Approach to Concurrent Design of Lowpower Transformers. Journal of Materials Processing Technology, 128, 136–141.
17. [17] Peters, M.L., and Zelewski, S. 2007. TOPSIS als Technik zur Effieienzanalyse. Zeitschrift für Ausbildung und Hochschulkontakt, 1-9.
18. [18] Yue, Z. 2011. A Method for Group Decision-Making Based on Determining Weights of Decision Makers Using TOPSIS. Applied Mathematical Modelling, 35-4, 1926-1936.
19. [19] Janic, M. 2003. Multicriteria Evaluation of High Speed Rail, Transrapid Maglev and Air Passenger Transport in Europa. Transportion Planning & Technology, 26(6), 491-512.
20. [20] Ozturk, E., Karaboyacı, M., Yetis, U., Yigit, N.O., Kitis, M. 2016. Minimization of Water and Chemical Use in a Cotton/Polyester Fabric Dyeing Textile Mill. Journal of Cleaner Production, 130, 92-102.
21. [21] Alkaya, E., Demirer G. N. 2014. Sustainable Textile Production: a Case Study From a Woven Fabric Manufacturing Mill in Turkey. Journal of Cleaner Production, 65, 595-603.
22. [22] European Commision (EC), 2009. Reference Document on The Best Available Techniques in Energy Efficiency, Spain.
23. [23] Öztürk, E. 2014. Tekstil Sektöründe Entegre Kirlilik Önleme ve Kontrolü ve Temiz Üretim Uygulamaları. Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Doktora Tezi, Isparta.
24. [24] Kiran, C.N. 2003. Reduction in Resource Consumption by Process Modifivations in Cotton Wet Process. Journal of Cleaner Production, 11, 481-486.
25. [25] Cheng, S., Chan, C.W., Huang, G. H. 2002. Using Multiple Criteria Decision Analysis for Supporting Decisions of Solid Waste Management. Journal of Enviromental Science Health, Part A, 37(6), 975-990.