Kinetic Evaluation of Ozonation and Advanced Oxidation Processes in Colorant-Heavy Textile Wastewater

References

[1] Adeoba, M. I. (2018). Phylogenetic analysis of extinction risk and diversification history of the African Cyprinidae using DNA barcodes (Doctoral dissertation, University of Johannesburg).

[2] Adeoba, M. I., & Yessoufou, K. (2018). Analysis of temporal diversification of African Cyprinidae (Teleostei, Cypriniformes). ZooKeys, (806), 141.

[3] Adeoba, M. I., Kabongo, R., Van der Bank, H., & Yessoufou, K. (2018). Re-evaluation of the discriminatory power of DNA barcoding on some specimens of African Cyprinidae (subfamilies Cyprininae and Danioninae). ZooKeys, (746), 105.

[4] Adeoba, M., Tesfamichael, S. G., & Yessoufou, K. (2019). Preserving the tree of life of the fish family Cyprinidae in Africa in the face of the ongoing extinction crisis. Genome, 62(3), 170-182.

[5] Adewoyin, M. A. (2021). Developing frameworks for managing low-carbon energy transitions: overcoming barriers to implementation in the oil and gas industry.

[6] Affognon, H., Mutungi, C., Sanginga, P., & Borgemeister, C. (2015). Unpacking postharvest losses in sub-Saharan Africa: a meta-analysis. World development, 66, 49-68.

[7] Afolabi, S. O., & Akinsooto, O. (2021). Theoretical framework for dynamic mechanical analysis in material selection for high-performance engineering applications. Noûs, 3.

[8] Agho, G., Ezeh, M. O., Isong, M., Iwe, D., & Oluseyi, K. A. (2021). Sustainable pore pressure prediction and its impact on geo-mechanical modelling for enhanced drilling operations. World Journal of Advanced Research and Reviews, 12(1), 540–557. https://doi.org/10.30574/wjarr.2021.12.1.0536

[9] Ahiaba, U. V. (2019). The Role of Grain Storage Systems in Food Safety, Food Security and Rural Development in Northcentral Nigeria (Doctoral dissertation, University of Gloucestershire).

[10] Ajayi, A. B., Afolabi, O., Folarin, T. E., Mustapha, H., & Popoola, A. (2020). Development of a low-cost polyurethane (foam) waste shredding machine. ABUAD Journal of Engineering Research and Development, 3(2), 105-14.

[11] Ajayi, A. B., Mustapha, H. A., Popoola, A. F., Folarin, T. E., & Afolabi, S. O. (2021). Development of a rectangular mould with vertical screw press for polyurethane (foam) waste recycling machine. polyurethane, 4(1).

[12] Ajayi, A. B., Popoola, A. F., Mustapha, H. A., Folarin, T. E., & Afolabi, S. O. (2020). Development of a mixer for polyurethane (foam) waste recycling machine. ABUAD Journal of Engineering Research and Development. Accepted (13/11/2020) in-Press. http://ajerd. abuad. edu. ng/papers.

[13] Ajibola, K. A., & Olanipekun, B. A. (2019). Effect of access to finance on entrepreneurial growth and development in Nigeria among “YOU WIN” beneficiaries in SouthWest, Nigeria. Ife Journal of Entrepreneurship and Business Management, 3(1), 134-149.

[14] Akande, B., & Diei-Ouadi, Y. (2010). Post-harvest losses in small-scale fisheries. Food and Agriculture Organization of the United Nations.

[15] Akang, V. I., Afolayan, M. O., Iorpenda, M. J., & Akang, J. V. (2019, October). INDUSTRIALIZATION OF THE NIGERIAN ECONOMY: THE IMPERATIVES OF IMBIBING ARTIFICIAL INTELLIGENCE AND ROBOTICS FOR NATIONAL GROWTH AND DEVELOPMENT. In Proceedings of: 2nd International Conference of the IEEE Nigeria (p. 265).

[16] Alam, M. A., Ahad, A., Zafar, S., & Tripathi, G. (2020). A neoteric smart and sustainable farming environment incorporating blockchain‐based artificial intelligence approach. Cryptocurrencies and Blockchain Technology Applications, 197-213.

[17] Al-Besher, A., & Kumar, K. (2022). Use of artificial intelligence to enhance e-government services. Measurement: sensors, 24, 100484.

[18] An, H., Wilhelm, W. E., & Searcy, S. W. (2011). Biofuel and petroleum-based fuel supply chain research: a literature review. Biomass and Bioenergy, 35(9), 3763-3774.

[19] Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government information quarterly, 36(2), 358-367.

[20] Babatunde, A. I. (2019). Impact of supply chain in reducing fruit post-harvest waste in agric value chain in Nigeria. Electronic Research Journal of Social Sciences and Humanities, 1, 150-163.

[21] Belot, S. T. (2020). The state and impact of the Fourth Industrial Revolution on economic development.

[22] Biń, A. K., & Sobera-Madej, S. (2012). Comparison of the advanced oxidation processes (UV, UV/H2O2 and O3) for the removal of antibiotic substances during wastewater treatment. Ozone: science & engineering, 34(2), 136-139.

[23] Chaudhuri, A., Dukovska-Popovska, I., Subramanian, N., Chan, H. K., & Bai, R. (2018). Decision-making in cold chain logistics using data analytics: a literature review. The International Journal of Logistics Management, 29(3), 839-861.

[24] Danese, P., Romano, P., & Formentini, M. (2013). The impact of supply chain integration on responsiveness: The moderating effect of using an international supplier network. Transportation Research Part E: Logistics and Transportation Review, 49(1), 125-140.

[25] Daraojimba, A. I., Ubamadu, B. C., Ojika, F. U., Owobu, O., Abieba, O. A., & Esan, O. J. (2021, July). Optimizing AI models for cross-functional collaboration: A framework for improving product roadmap execution in agile teams. IRE Journals, 5(1), 14. ISSN: 2456-8880.

[26] Das Nair, R., & Landani, N. (2020). Making agricultural value chains more inclusive through technology and innovation (No. 2020/38). WIDER working paper.

[27] Dauvergne, P. (2022). Is artificial intelligence greening global supply chains? Exposing the political economy of environmental costs. Review of International Political Economy, 29(3), 696-718.

[28] De Almeida, P. G. R., dos Santos, C. D., & Farias, J. S. (2021). Artificial intelligence regulation: a framework for governance. Ethics and Information Technology, 23(3), 505-525.

[29] Djeffal, C., Siewert, M. B., & Wurster, S. (2022). Role of the state and responsibility in governing artificial intelligence: a comparative analysis of AI strategies. Journal of European Public Policy, 29(11), 1799-1821.

[30] Dong, Y., Hou, J., Zhang, N., & Zhang, M. (2020). Research on how human intelligence, consciousness, and cognitive computing affect the development of artificial intelligence. Complexity, 2020(1), 1680845.

[31] Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71.

[32] Edwards, Q., Mallhi, A. K., & Zhang, J. (2018). The association between advanced maternal age at delivery and childhood obesity. J Hum Biol, 30(6), e23143.

[33] Egbuhuzor, N. S., Ajayi, A. J., Akhigbe, E. E., Agbede, O. O., Ewim, C. P.-M., & Ajiga, D. I. (2021). Cloud-based CRM systems: Revolutionizing customer engagement in the financial sector with artificial intelligence. International Journal of Science and Research Archive, 3(1), 215-234. https://doi.org/10.30574/ijsra.2021.3.1.0111

[34] Egbumokei, P. I., Dienagha, I. N., Digitemie, W. N., & Onukwulu, E. C. (2021). Advanced pipeline leak detection technologies for enhancing safety and environmental sustainability in energy operations. International Journal of Science and Research Archive, 4(1), 222–228. https://doi.org/10.30574/ijsra.2021.4.1.0186

[35] Ezeanochie, C. C., Afolabi, S. O., & Akinsooto, O. (2021). A Conceptual Model for Industry 4.0 Integration to Drive Digital Transformation in Renewable Energy Manufacturing.

[36] Ezenwa, A. E. (2019). Smart logistics diffusion strategies amongst supply chain networks in emerging markets: a case of Nigeria's micro/SMEs 3PLs (Doctoral dissertation, University of Leeds).

[37] Francis Onotole, E., Ogunyankinnu, T., Adeoye, Y., Osunkanmibi, A. A., Aipoh, G., & Egbemhenghe, J. (2022). The Role of Generative AI in developing new Supply Chain Strategies-Future Trends and Innovations.

[38] Gianni, R., Lehtinen, S., & Nieminen, M. (2022). Governance of responsible AI: From ethical guidelines to cooperative policies. Frontiers in Computer Science, 4, 873437.

[39] Hassaan, M. A., El Nemr, A., & Madkour, F. F. (2016). Application of Ozonation and UV assisted Ozonation for Decolorization of Direct Yellow 50 in Sea water. The Pharmaceutical and Chemical Journal, 3(2).

[40] Helo, P., & Hao, Y. (2022). Artificial intelligence in operations management and supply chain management: An exploratory case study. Production Planning & Control, 33(16), 1573-1590.

[41] Hodges, R. J., Buzby, J. C., & Bennett, B. (2011). Postharvest losses and waste in developed and less developed countries: opportunities to improve resource use. The Journal of Agricultural Science, 149(S1), 37-45.

[42] Ijeomah, S. (2020). Challenges of supply chain management in the oil & gas production in Nigeria (Shell Petroleum Development Company of Nigeria) (Doctoral dissertation, Dublin, National College of Ireland).

[43] Ikeh, T. C., & Ndiwe, C. U. (2019). Solar photovoltaic as an option (alternative) for electrification of health care service in Anambra West, Nigeria. Asian Journal of Science and Technology, 10(6), 9720-9724. Asian Science & Technology.

[44] Ilori, M. O., & Olanipekun, S. A. (2020). Effects of government policies and extent of its implementations on the foundry industry in Nigeria. IOSR Journal of Business Management, 12(11), 52-59

[45] Imran, S., Patel, R. S., Onyeaka, H. K., Tahir, M., Madireddy, S., Mainali, P., ... & Ahmad, N. (2019). Comorbid depression and psychosis in Parkinson’s disease: a report of 62,783 hospitalizations in the United States. Cureus, 11(7).

[46] Isi, L. R., Ogu, E., Egbumokei, P. I., Dienagha, I. N., & Digitemie, W. N. (2021). Pioneering Eco-Friendly Fluid Systems and Waste Minimization Strategies in Fracturing and Stimulation Operations.

[47] Isi, L. R., Ogu, E., Egbumokei, P. I., Dienagha, I. N., & Digitemie, W. N. (2021). Advanced Application of Reservoir Simulation and DataFrac Analysis to Maximize Fracturing Efficiency and Formation Integrity.

[48] Jagtap, S., Bader, F., Garcia-Garcia, G., Trollman, H., Fadiji, T., & Salonitis, K. (2020). Food logistics 4.0: Opportunities and challenges. Logistics, 5(1), 2.

[49] Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.

[50] Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(01), 83-111.

[51] Kandziora, C. (2019, April). Applying artificial intelligence to optimize oil and gas production. In Offshore Technology Conference (p. D021S016R002). OTC.

[52] Kankanhalli, A., Charalabidis, Y., & Mellouli, S. (2019). IoT and AI for smart government: A research agenda. Government Information Quarterly, 36(2), 304-309.

[53] Khalifa, N., Abd Elghany, M., & Abd Elghany, M. (2021). Exploratory research on digitalization transformation practices within supply chain management context in developing countries specifically Egypt in the MENA region. Cogent Business & Management, 8(1), 1965459.

[54] Kolade, O., Osabuohien, E., Aremu, A., Olanipekun, K. A., Osabohien, R., & Tunji-Olayeni, P. (2021). Co-creation of entrepreneurship education: challenges and opportunities for university, industry and public sector collaboration in Nigeria. The Palgrave Handbook of African Entrepreneurship, 239-265.

[55] Kolade, O., Rae, D., Obembe, D., & Woldesenbet, K. (Eds.). (2022). The Palgrave handbook of African entrepreneurship. Palgrave Macmillan.

[56] Koroteev, D., & Tekic, Z. (2021). Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future. Energy and AI, 3, 100041.

[57] Korteling, J. H., van de Boer-Visschedijk, G. C., Blankendaal, R. A., Boonekamp, R. C., & Eikelboom, A. R. (2021). Human-versus artificial intelligence. Frontiers in artificial intelligence, 4, 622364.

[58] Krishnan, A., Banga, K., & Mendez-Parra, M. (2020). Disruptive technologies in agricultural value chains. Insights from East Africa. Working paper, 576.

[59] Kuang, L., He, L. I. U., Yili, R. E. N., Kai, L. U. O., Mingyu, S. H. I., Jian, S. U., & Xin, L. I. (2021). Application and development trend of artificial intelligence in petroleum exploration and development. Petroleum Exploration and Development, 48(1), 1-14.

[60] Kumar, D., Singh, R. K., Mishra, R., & Wamba, S. F. (2022). Applications of the internet of things for optimizing warehousing and logistics operations: A systematic literature review and future research directions. Computers & Industrial Engineering, 171, 108455.

[61] Lin, H., Lin, J., & Wang, F. (2022). An innovative machine learning model for supply chain management. Journal of Innovation & Knowledge, 7(4), 100276.

[62] Lu, Y. (2019). Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1-29.

[63] Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of things Journal, 9(9), 6305-6324.

[64] Morris, K. J., Kamarulzaman, N. H., & Morris, K. I. (2019). Small-scale postharvest practices among plantain farmers and traders: A potential for reducing losses in rivers state, Nigeria. Scientific African, 4, e00086.

[65] Mwangi, N. W. (2019). Influence of supply chain optimization on the performance of manufacturing firms in Kenya (Doctoral dissertation, JKUAT-COHRED).

[66] Nahr, J. G., Nozari, H., & Sadeghi, M. E. (2021). Green supply chain based on artificial intelligence of things (AIoT). International Journal of Innovation in Management, Economics and Social Sciences, 1(2), 56-63.

[67] Negi, S. (2021). Supply chain efficiency framework to improve business performance in a competitive era. Management Research Review, 44(3), 477-508.

[68] Ochinanwata, N. H. (2019). Integrated business modelling for developing digital internationalising firms in Nigeria (Doctoral dissertation, Sheffield Hallam University).

[69] Odio, P. E., Kokogho, E., Olorunfemi, T. A., Nwaozomudoh, M. O., Adeniji, I. E., & Sobowale, A. (2021). Innovative financial solutions: A conceptual framework for expanding SME portfolios in Nigeria's banking sector. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 495-507.

[70] Ofori-Asenso, R., Ogundipe, O., Agyeman, A. A., Chin, K. L., Mazidi, M., Ademi, Z., ... & Liew, D. (2020). Cancer is associated with severe disease in COVID-19 patients: a systematic review and meta-analysis. Ecancermedicalscience, 14, 1047.

[71] Ofori-Asenso, R., Ogundipe, O., Agyeman, A. A., Chin, K. L., Mazidi, M., Ademi, Z., ... & Liew, D. (2020). Cancer is associated with severe disease in COVID-19 patients: a systematic review and meta-analysis. Ecancermedicalscience, 14, 1047.

[72] Ogundipe, O., Mazidi, M., Chin, K. L., Gor, D., McGovern, A., Sahle, B. W., ... & Ofori-Asenso, R. (2021). Real-world adherence, persistence, and in-class switching during use of dipeptidyl peptidase-4 inhibitors: a systematic review and meta-analysis involving 594,138 patients with type 2 diabetes. Acta Diabetologica, 58, 39-46.

[73] Ogunnowo, E., Ogu, E., Egbumokei, P., Dienagha, I., & Digitemie, W. (2021). Theoretical framework for dynamic mechanical analysis in material selection for high-performance engineering applications. Open Access Research Journal of Multidisciplinary Studies, 1(2), 117-131.

[74] Ogunyankinnu, T., Onotole, E. F., Osunkanmibi, A. A., Adeoye, Y., Aipoh, G., & Egbemhenghe, J. (2022). Blockchain and AI synergies for effective supply chain management.

[75] Ojika, F. U., Owobu, O., Abieba, O. A., Esan, O. J., Daraojimba, A. I., & Ubamadu, B. C. (2021, March). A conceptual framework for AI-driven digital transformation: Leveraging NLP and machine learning for enhanced data flow in retail operations. IRE Journals, 4(9). ISSN: 2456-8880.

[76] Ojika, F. U., Owobu, W. O., Abieba, O. A., Esan, O. J., Ubamadu, B. C., & Ifesinachi, A. (2021). Optimizing AI Models for Cross-Functional Collaboration: A Framework for Improving Product Roadmap Execution in Agile Teams.

[77] Okolo, F. C., Etukudoh, E. A., Ogunwole, O., Osho, G. O., & Basiru, J. O. (2021). Systematic Review of Cyber Threats and Resilience Strategies Across Global Supply Chains and Transportation Networks.

[78] Olanipekun, K. A. (2020). Assessment of Factors Influencing the Development and Sustainability of Small Scale Foundry Enterprises in Nigeria: A Case Study of Lagos State. Asian Journal of Social Sciences and Management Studies, 7(4), 288-294.

[79] Olanipekun, K. A., & Ayotola, A. (2019). Introduction to marketing. GES 301, Centre for General Studies (CGS), University of Ibadan.

[80] Olanipekun, K. A., Ilori, M. O., & Ibitoye, S. A. (2020): Effect of Government Policies and Extent of Its Implementation on the Foundry Industry in Nigeria.

[81] Olisah, M. C. (2023). Enhancing the supply chain collaboration model in the Nigerian oil and gas industry: a case study of performance improvement strategies.

[82] Olukunle, O. T. (2013). Challenges and prospects of agriculture in Nigeria: the way forward. Journal of Economics and sustainable development, 4(16), 37-45.

[83] Omisola, J. O., Etukudoh, E. A., Okenwa, O. K., & Tokunbo, G. I. (2020). Innovating Project Delivery and Piping Design for Sustainability in the Oil and Gas Industry: A Conceptual Framework. perception, 24, 28-35.

[84] Omisola, J. O., Etukudoh, E. A., Okenwa, O. K., & Tokunbo, G. I. (2020). Innovating Project Delivery and Piping Design for Sustainability in the Oil and Gas Industry: A Conceptual Framework. perception, 24, 28-35.

[85] Onaghinor, O., Uzozie, O. T., Esan, O. J., Etukudoh, E. A., & Omisola, J. O. (2021). Predictive modeling in procurement: A framework for using spend analytics and forecasting to optimize inventory control. IRE Journals, 5(6), 312–314.

[86] Onaghinor, O., Uzozie, O. T., Esan, O. J., Osho, G. O., & Etukudoh, E. A. (2021). Gender-responsive leadership in supply chain management: A framework for advancing inclusive and sustainable growth. IRE Journals, 4(7), 135–137.

[87] Onukwulu, E. C., Dienagha, I. N., Digitemie, W. N., & Egbumokei, P. I. (2021, June 30). Framework for decentralized energy supply chains using blockchain and IoT technologies. IRE Journals. https://www.irejournals.com/index.php/paper-details/1702766

[88] Onukwulu, E. C., Dienagha, I. N., Digitemie, W. N., & Egbumokei, P. I. (2021, September 30). Predictive analytics for mitigating supply chain disruptions in energy operations. IRE Journals. https://www.irejournals.com/index.php/paper-details/1702929

[89] Onukwulu, E. C., Dienagha, I. N., Digitemie, W. N.,& Egbumokei, P. I (2021). AI-driven supply chain optimization for enhanced efficiency in the energy sector. Magna Scientia Advanced Research and Reviews, 2(1) 087-108 https://doi.org/10.30574/msarr.2021.2.1.0060

[90] Orieno, O. H., Oluoha, O. M., Odeshina, A., Reis, O., Okpeke, F., & Attipoe, V. (2021). Project management innovations for strengthening cybersecurity compliance across complex enterprises. Open Access Research Journal of Multidisciplinary Studies, 2(1), 871–881.

[91] Otokiti, B. O., Igwe, A. N., Ewim, C. P., Ibeh, A. I., & Sikhakhane-Nwokediegwu, Z. (2022). A framework for developing resilient business models for Nigerian SMEs in response to economic disruptions. Int J Multidiscip Res Growth Eval, 3(1), 647-659.

[92] Otuoze, S. H., Hunt, D. V., & Jefferson, I. (2021). Neural network approach to modelling transport system resilience for major cities: case studies of lagos and kano (Nigeria). Sustainability, 13(3), 1371.

[93] Oyedokun, O. O. (2019). Green human resource management practices and its effect on the sustainable competitive edge in the Nigerian manufacturing industry (Dangote) (Doctoral dissertation, Dublin Business School).

[94] Oyewola, D. O., Dada, E. G., Omotehinwa, T. O., Emebo, O., & Oluwagbemi, O. O. (2022). Application of deep learning techniques and bayesian optimization with tree parzen estimator in the classification of supply chain pricing datasets of health medications. Applied Sciences, 12(19), 10166.

[95] Qi, Y., Huo, B., Wang, Z., & Yeung, H. Y. J. (2017). The impact of operations and supply chain strategies on integration and performance. International Journal of Production Economics, 185, 162-174.

[96] Qrunfleh, S., & Tarafdar, M. (2014). Supply chain information systems strategy: Impacts on supply chain performance and firm performance. International journal of production economics, 147, 340-350.

[97] Raja Santhi, A., & Muthuswamy, P. (2022). Pandemic, war, natural calamities, and sustainability: Industry 4.0 technologies to overcome traditional and contemporary supply chain challenges. Logistics, 6(4), 81.

[98] Ramdoo, I., Cosbey, A., Geipel, J., & Toledano, P. (2021). New Tech, New Deal: Mining policy options in the face of new technology.

[99] Richey, R. G., Roath, A. S., Adams, F. G., & Wieland, A. (2022). A responsiveness view of logistics and supply chain management. Journal of Business Logistics, 43(1), 62-91.

[100] Sanusi, I. T. (2023). Machine learning education in the K–12 Context.

[101] Shah, N. K., Li, Z., & Ierapetritou, M. G. (2011). Petroleum refining operations: key issues, advances, and opportunities. Industrial & Engineering Chemistry Research, 50(3), 1161-1170.

[102] Sibanda, S., & Workneh, T. S. (2020). Potential causes of postharvest losses, low-cost cooling technology for fresh produce farmers in Sub-Sahara Africa. African Journal of Agricultural Research, 16(5), 553-566.

[103] Simchi‐Levi, D., Wang, H., & Wei, Y. (2018). Increasing supply chain robustness through process flexibility and inventory. Production and Operations Management, 27(8), 1476-1491.

[104] Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021). Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379-391.

[105] Sobowale, A., Nwaozomudoh, M. O., Odio, P. E., Kokogho, E., Olorunfemi, T. A., & Adeniji, I. E. (2021). Developing a conceptual framework for enhancing interbank currency operation accuracy in Nigeria's banking sector. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 481–494. ANFO Publication House.

[106] Sobowale, A., Odio, P. E., Kokogho, E., Olorunfemi, T. A., Nwaozomudoh, M. O., & Adeniji, I. E. (2021). Innovative financial solutions: A conceptual framework for expanding SME portfolios in Nigeria's banking sector. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 495–507. ANFO Publication House.

[107] Stathers, T., & Mvumi, B. (2020). Challenges and initiatives in reducing postharvest food losses and food waste: sub-Saharan Africa. In Preventing food losses and waste to achieve food security and sustainability (pp. 729-786). Burleigh Dodds Science Publishing.

[108] Suryawan, I. W. K., Prajati, G., Afifah, A. S., & Apritama, M. R. (2021). NH3-N and COD reduction in Endek (Balinese textile) wastewater by activated sludge under different DO condition with ozone pretreatment. Walailak Journal of Science and Technology (WJST), 18(6), 9127-11.

[109] Taeihagh, A. (2021). Governance of artificial intelligence. Policy and society, 40(2), 137-157.

[110] Talla, R. R. (2022). Integrating Blockchain and AI to Enhance Supply Chain Transparency in Energy Sectors. Asia Pacific Journal of Energy and Environment, 9(2), 109-118.

[111] Terziyan, V., Gryshko, S., & Golovianko, M. (2018). Patented intelligence: Cloning human decision models for Industry 4.0. Journal of manufacturing systems, 48, 204-217.

[112] Tien, J. M. (2017). Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science, 4, 149-178.

[113] Tien, N. H., Anh, D. B. H., & Thuc, T. D. (2019). Global supply chain and logistics management.

[114] Truby, J. (2020). Governing artificial intelligence to benefit the UN sustainable development goals. Sustainable Development, 28(4), 946-959.

[115] Tula, O. A., Adekoya, O. O., Isong, D., Daudu, C. D., Adefemi, A., & Okoli, C. E. (2004). Corporate advising strategies: A comprehensive review for aligning petroleum engineering with climate goals and CSR commitments in the United States and Africa. Corporate Sustainable Management Journal, 2(1), 32-38.

[116] Urciuoli, L., Mohanty, S., Hintsa, J., & Gerine Boekesteijn, E. (2014). The resilience of energy supply chains: a multiple case study approach on oil and gas supply chains to Europe. Supply Chain Management: An International Journal, 19(1), 46-63.

[117] Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International journal of production economics, 176, 98-110.

[118] West, M., Kraut, R., & Ei Chew, H. (2019). I'd blush if I could: closing gender divides in digital skills through education.

[119] Yigitcanlar, T., Corchado, J. M., Mehmood, R., Li, R. Y. M., Mossberger, K., & Desouza, K. (2021). Responsible urban innovation with local government artificial intelligence (AI): A conceptual framework and research agenda. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 71.

[120] Yigitcanlar, T., Mehmood, R., & Corchado, J. M. (2021). Green artificial intelligence: Towards an efficient, sustainable and equitable technology for smart cities and futures. Sustainability, 13(16), 8952.

[121] Yue, D., You, F., & Snyder, S. W. (2014). Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges. Computers & chemical engineering, 66, 36-56.

[122] Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224.

[123] Zohuri, B., & Moghaddam, M. (2020). From business intelligence to artificial intelligence. Journal of Material Sciences & Manufacturing Research, 1(1), 1-10.

@article{1708810,
      author = {Matluck Afolabi, Ogechi Amanda Onukogu, Thompson Odion Igunma, Adeniyi K. Adeleke, Zamathula Q. Sikhakhane Nwokediegwu},
      title = {Kinetic Evaluation of Ozonation and Advanced Oxidation Processes in Colorant-Heavy Textile Wastewater},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {2},
      pages = {235-260},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708810.pdf},
      abstract = {The treatment of colorant-heavy textile wastewater remains a critical environmental challenge due to the complex molecular structures and recalcitrant nature of synthetic dyes. This study presents a kinetic evaluation of ozonation and advanced oxidation processes (AOPs), specifically ozone/hydrogen peroxide (O?/H?O?) and Fenton-based systems, for the degradation of persistent dyes in textile effluents. The research investigates the reaction mechanisms, degradation efficiencies, and rate constants under varying operational conditions, including pH, oxidant dosage, and initial dye concentration. Experimental results indicate that while single ozonation exhibits rapid initial decolorization, it is less effective in mineralizing dye molecules, as evidenced by lower chemical oxygen demand (COD) removal. Conversely, the integration of ozone with hydrogen peroxide significantly enhances hydroxyl radical generation, leading to higher reaction rates and broader contaminant breakdown. Kinetic data were fitted to pseudo-first-order and second-order models to determine the most accurate description of each system?s behavior. The pseudo-first-order model provided a better fit for ozonation alone, whereas the combined AOPs showed improved correlation with second-order kinetics, suggesting increased complexity in reactive species interactions. The Fenton process demonstrated high efficiency at acidic pH levels, but its performance was highly sensitive to Fe??/H?O? ratios and sludge formation. Overall, the O?/H?O? system achieved the highest overall kinetic rate and mineralization efficiency with minimal sludge production, making it a promising alternative for scalable industrial deployment. The study underscores the importance of kinetic evaluation in optimizing AOPs for real-world textile wastewater applications. It further emphasizes that understanding degradation dynamics at the molecular level is essential for achieving sustainable and cost-effective effluent treatment solutions. Future research should focus on pilot-scale validation, integration with membrane or biological systems, and the development of intelligent control frameworks for operational efficiency.},
      keywords = {Textile Wastewater, Ozonation, Advanced Oxidation Processes, AOP Kinetics, Dye Degradation, Hydroxyl Radicals, Pseudo-First-Order, Fenton Process, O?/H?O? System, COD Removal.},
      month = {August},
  }