Category Archives: Engineering and Scientific International Journal (ESIJ)

The Significance of Special Schools in the Provision of Quality Education for Learners with Severe Learning Difficulties in Zambia: AI Assistive Technologies, Specialised Pedagogy and Inclusive Development

Author
Ngenda Mukuba, Dr. Siyumbwa Costa
Keywords

Special Schools; Severe Learning Difficulties; Zambia; Inclusive Education; AI Assistive Technology; Specialised Pedagogy; Disability; Quality Education.

Abstract

The debate between special school provision and inclusive mainstream schooling for learners with severe learning difficulties remains one of the most contested questions in contemporary special education policy. While inclusive education has achieved dominant policy status globally, special schools continue to provide specialised educational environments for learners whose needs including severe intellectual disability, profound sensory impairment, autism spectrum disorder, and multiple disabilities may not be adequately met within mainstream inclusive settings. This article examines the significance of special schools in providing quality education for learners with severe learning difficulties in Zambia, contextualising findings within global scholarship on special education models, AI-powered assistive technologies, specialised pedagogical approaches, and disability-inclusive development frameworks. Drawing on a descriptive survey of special school educators, parents, and education administrators, findings confirm that special schools make substantial contributions to learner academic progress, social development, and family support when adequately resourced and pedagogically equipped but are severely constrained by resource scarcity, teacher capacity deficits, and limited AI assistive technology access. The article argues that AI-powered specialised learning platforms, augmentative communication tools, and community-based transition programmes offer transformative pathways for enhancing special school quality. Policy recommendations are presented.

References
[1] Akila, V., Prabhu, G., Akila, R., & Swadhi, R. (2025). Performance metrics in blockchain-enabled AIML for cognitive IoT in large-scale networks. In AI for large scale communication networks (pp. 265–288). IGI Global Scientific Publishing.
[2] Arockia, V. J., Vettriselvan, R., Rajesh, D., Velmurugan, P. R. R., & Cheelo, C. (2025). Leveraging AI and learning analytics for enhanced distance learning. In AI and learning analytics in distance learning (pp. 179–206). IGI Global Scientific Publishing.
[3] Ashifa, K. M. (2019). Developmental initiatives for persons with disabilities. Indian Journal of Public Health Research & Development, 10(12), 1257–1261.
[4] Ashifa, K. M. (2020a). Effect of substance abuse on physical health of adolescents. European Journal of Molecular & Clinical Medicine, 7(2), 3155–3160.
[5] Ashifa, K. M. (2020b). Physical health hazards of schizophrenia patients. Systematic Reviews in Pharmacy, 11(12), 1848–1850.
[6] Ashifa, K. M. (2021a). Analysis on the determinants of health status among tribal communities. Journal of Cardiovascular Disease Research, 12(3), 531–534.
[7] Ashifa, K. M. (2021b). Health status of primitive tribal women in India. Journal of Cardiovascular Disease Research, 12(5), 772.
[8] Ashifa, K. M. (2022). A situation analysis of the social well-being of elderly during the COVID-19 pandemic. International Journal of Health Sciences, 6(3), 10156–10163.
[9] Ashifa, K. M., & Ramya, P. (2019). Health afflictions and quality of work life among women working in fireworks industry. International Journal of Engineering and Advanced Technology, 8(6S3), 1723–1725.
[10] Basha, R., Pathak, P., Sudha, M., Soumya, K. V., & Arockia Venice, J. (2025). Optimization of quantum dilated convolutional neural networks: Image recognition with quantum computing. Internet Technology Letters, 8(3), e70027.
[11] Devi, M., Manokaran, D., Sehgal, R. K., Shariff, S. A., & Vettriselvan, R. (2025). Precision medicine, personalized treatment, and network-driven innovations. In AI for large scale communication networks (pp. 303–322). IGI Global.
[12] Elkin, N., Mohammed, A. K., Kılınçel, Ş., Soydan, A. M., Tanrıver, S. Ç., Çelik, Ş., & Ranganathan, M. (2025). Mental health literacy and happiness among university students. Frontiers in Psychiatry, 16, 1541316.
[13] Gayathri, R. K., Vettriselvan, R., Rajesh, D., Balakrishnan, R., Kumar, R., & Kavitha, J. (2025a). Striking a balance: Mental health challenges and work-life integration among women faculty in Indian B-Schools. Texila International Journal of Public Health, 13(2).
[14] Gayathri, R. K., Vettriselvan, R., Rajesh, D., Balakrishnan, R., Kumar, R., & Kavitha, J. (2025b). Strategic role of human resource management in enhancing occupational health and safety practices. Texila International Journal of Public Health, 13(2).
[15] Jenifer, R. D., Vettriselvan, R., Saxena, D., Velmurugan, P. R., & Balakrishnan, A. (2025). Green marketing in healthcare advertising: A global perspective. In AI impacts on branded entertainment and advertising (pp. 303–326). IGI Global.
[16] Kariveliparambil, A., Rasi, R. A., Ahmad, M. S., Öztaş, N., & Ayan, F. S. (2026a). Evolving social capital in indigenous communities. Journal of Social Service Research, 52(1), 147–166.
[17] Kariveliparambil, A., R A, R., Ahmad, M. S., Ramesh, S., & Kuriakose, A. (2026b). Invisible burdens of platform work. International Journal of Qualitative Studies on Health and Well-Being, 21(1).
[18] Kombo, D. K., & Tromp, D. L. A. (2014). Proposal and thesis writing: An introduction. Paulines Publications Africa.
[19] Meena, G., Vettriselvan, R., Rajesh, D., & Velmurugan, P. R. (2025). Diversity and inclusion: Harnessing the power of inclusivity for business success. In Security and strategy models for key-solving institutional frameworks (pp. 203–234). IGI Global Scientific Publishing.
[20] Mohanbabu, S., & Vettriselvan, R. (2025a). Focusing supply chain and container terminal challenges. International Journal of Procurement Management, 24(1), 92–114.
[21] Mohanbabu, S., & Vettriselvan, R. (2025b). Will machine learning resolve the issues in container management. International Journal of Process Management and Benchmarking, 20(4), 559–575.
[22] Orodho, J. A., & Kombo, D. K. (2012). Research methods. Kenyatta University Press.
[23] Rajeswari, M., Rohini, V., Sathya Aarthi, R., Rameshkumaar, V. P., & Arul Krishnan, S. (2026). Blockchain 2.0 for secure, transparent, and autonomous logistics systems. In R. Vettriselvan & N. Suresh (Eds.), Intelligent motion control for human-centered systems (pp. 233–258). IGI Global Scientific Publishing.
[24] Ranganathan, M., Jacob, A., Ashifa, K. M., Kumar, G. J., Anthony, M., Vijay, M., & Kumari, R. B. (2024). An investigation of the effects of chronic stress on attention in parents of children with neurodevelopmental disorders. Universal Journal of Public Health, 12(1), 37–50.
[25] Rasi, R. A., & Ashifa, K. M. (2019). Role of community-based programmes for active ageing. Indian Journal of Public Health Research & Development, 10(12).
[26] Shanthi, H. J., Gokulakrishnan, A., Sharma, S., Deepika, R., & Swadhi, R. (2025). Leveraging artificial intelligence for enhancing urban health. In Nexus of AI, climatology, and urbanism for smart cities (pp. 275–306). IGI Global.
[27] Swadhi, R., Gayathri, K., Suresh, N. V., Catherine, S., & Velmurugan, P. R. (2025a). Leveraging machine learning for enhanced patient engagement and outcomes. In Impact of digital transformation on business growth and performance (pp. 313–340). IGI Global Scientific Publishing.
[28] Swadhi, R., Velmurugan, P. R., Gayathri, K., & Catherine, S. (2025b). Evolving critical themes in advanced human resource management. In Critical aspects in advanced human resource management (pp. 75–102). IGI Global Scientific Publishing.
[29] Vasantha, S., Swadhi, R., Gayathri, K., Selvalakshmi, V., & UmaDevi, A. (2025). Fostering personalized learning and achieving equity in education. In Transforming education with AI-powered personalized learning (pp. 201–236). IGI Global Scientific Publishing.
[30] Venice, J. A., Arivazhagan, D., Suman, N., Shanthi, H. J., & Swadhi, R. (2025a). Recommendation systems and content personalization. In AI for large scale communication networks (pp. 323–348). IGI Global Scientific Publishing.
[31] Venice, J. A., Vettriselvan, R., Jain, S., Madusudanan, K., & Aarthy, C. C. J. (2025b). Performance evaluation and metrics in blockchain powered AI/ML. In Transforming education with AI-powered personalized learning (pp. 143–178). IGI Global Scientific Publishing.
[32] Venice, J. A., Vettriselvan, R., Rajesh, D., Suresh, N. V., & Abirami, P. (2025c). Enabling personalized learning and adaptive systems through strategic management. In Bridging academia and industry through cloud integration in education (pp. 49–72). IGI Global Scientific Publishing.
[33] Venice, J. A., Vettriselvan, R., Rajesh, D., Xavier, P., & Shanthi, H. J. (2025d). Optimizing performance metrics in blockchain-enabled AI/ML data analytics. In Enhancing automated decision-making through AI (pp. 97–122). IGI Global.
[34] Venice, J. A., Sripathi, S. K., & Moonga, B. (2025e). Social deviance and the influence of internet exposure. ASET Journal of Management Science, 4(SI-1).
[35] Venice, J. A. A., Jio, W., Kant, S., Sharda, S., & Mittal, S. (2025f). Ethical leadership effect on the regulation of AI in cyber security. In Ethical challenges of AI and warfare (pp. 133–152). IGI Global Scientific Publishing.
[36] Venice, J. A. A., Muthuraman, M., Kant, S., & Mittal, S. (2026). Community engagement effect on school leadership through digital volunteerism. In Strengthening community engagement and school leadership through digital volunteerism (pp. 85–114). IGI Global Scientific Publishing.
[37] Vettriselvan, R. (2025). Harnessing innovation and digital marketing in the era of industry 5.0. In The future of small business in industry 5.0 (pp. 163–186). IGI Global.
[38] Vettriselvan, R., & Anto, M. R. (2018). Pathetic health status and working condition of Zambian women. Indian Journal of Public Health Research & Development, 9(9), 259–264.
[39] Vettriselvan, R., & Rajan FSA, A. J. (2019). Occupational health issues faced by women in spinners. Indian Journal of Public Health Research & Development, 10(1).
[40] Vettriselvan, R., Deepan, A., Jaiswani, G., Balakrishnan, A., & Sakthivel, R. (2025a). Health consequences of early marriage. In Social, political, and health implications of early marriage (pp. 189–212). IGI Global.
[41] Vettriselvan, R., Velmurugan, P. R., Varshney, K. R., EP, J., & Deepika, R. (2025b). Health impacts of smartphone and internet addictions across age groups. In Impacts of digital technologies across generations (pp. 187–210). IGI Global.
[42] Vettriselvan, R., Velmurugan, P. R., Suresh, N. V., & Catherine, S. (2025c). Strategies, best practices, and pitfalls in the era of digital transformation. In Impact of digital transformation on business growth and performance (pp. 67–98). IGI Global Scientific Publishing.
[43] Vettriselvan, R., Selvi, K., Kumar, A. S., Ranjani, R. D., & Varshney, K. R. (2025d). Ranking methodologies: Criteria and controversies in global higher education. In Global university ranking systems (pp. 109–140). IGI Global Scientific Publishing.
[44] Vettriselvan, R., Gokuldas, P. G., & Sambamoorthy, N. (2025e). Designing language materials to motivate, engage, and empower learners. In Exploring the psychology of language materials development (pp. 279–302). IGI Global Scientific Publishing.
[45] Vettriselvan, R., Ramya, R., Selvalakshmi, V., Jyothi, P., & Velmurugan, P. R. (2026a). Empowering patients through knowledge: Educational strategies in rehabilitation. In Holistic approaches to health recovery (pp. 263–290). IGI Global Scientific Publishing.
[46] Vettriselvan, R., Velmurugan, P. R., Savariapitchai, M., & Swadhi, R. (2026b). AI and international volunteering. In Impacts of AI on international volunteering (pp. 1–24). IGI Global Scientific Publishing.
[47] Vijayalakshmi, M., Subramani, A. K., Vettriselvan, R., Catherin, T. C., & Deepika, R. (2025a). Sustainability and responsibility in the digital era. In Digital citizenship and building a responsible online presence (pp. 285–306). IGI Global.
[48] Vijayalakshmi, M., Subramani, A. K., Vettriselvan, R., Velmurugan, P. R., & Hasine, J. (2025b). Strategic collaborations in medical innovation and AI-driven globalization. In Navigating strategic partnerships for sustainable startup growth (pp. 85–110). IGI Global.
[49] Vinodh, N., Subramani, A. K., & Vettriselvan, R. (2026a). Navigating ethics, society, and governance in the digital age. In Ethics, justice, and governance in the age of AI and digital societies (pp. 1–26). IGI Global Scientific Publishing.
[50] Vinodh, N., Subramani, A. K., & Vettriselvan, R. (2026b). Transforming the future of management and medical education. In AI education strategies for future-proofing curriculum design (pp. 459–476). IGI Global Scientific Publishing.
[51] Zahoor, H., Mustafa, N., Ashifa, K. M., Safaei, M., & El Gamil, R. (2025). Unlocking resilience: Emotional intelligence and self-leadership shape stress perception among health students. International Journal of Innovation and Learning, 38(4), 395–419.

Received : 21 February 2026
Accepted : 25 April 2026
Published : 30 April 2026
DOI: 10.30726/esij/v13.i2.2026.1320018

18.-25.-Ngenda-Mukuba.pdf

Causes of Truancy among Learners and its Impact on Education in Sikongo District, Zambia: AI-Driven Attendance Monitoring, Community Engagement and Inclusive School Strategies

Author
Nasilele Lubinda, Dr. J. Arockia Venice
Keywords
Truancy; School Attendance; Sikongo District; Zambia; AI Attendance Monitoring; Community Engagement; Inclusive Schools, Absenteeism.
Abstract
Truancy the persistent, unauthorised absence of learners from school is a pervasive challenge in Zambian primary and secondary schools, with documented consequences for academic performance, educational attainment, social development, and long-term life outcomes. In Sikongo District, Western Province, Zambia, truancy among primary school learners represents a significant barrier to achieving the educational goals of universal primary education and equitable learning outcomes. This article investigates the causes of truancy among learners and its impact on educational quality in two selected schools in Sikongo District, contextualising findings within global scholarship on school attendance, AI-driven attendance monitoring systems, community-school engagement strategies, and inclusive school approaches. Drawing on a mixed-methods survey, findings identify poverty-related economic pressures, family dysfunction, negative school experiences including teacher punishment, geographic distance to school, and peer influence as primary truancy drivers. Academic performance deficits, social exclusion, and elevated school dropout risk are documented as key educational impacts. AI-powered early warning attendance systems, community school engagement platforms, and inclusive school climate interventions are identified as evidence-based responses. Policy recommendations are presented.
References
[1] Akila, V., Prabhu, G., Akila, R., & Swadhi, R. (2025). Performance metrics in blockchain-enabled AIML for cognitive IoT in large-scale networks. In AI for large scale communication networks (pp. 265–288). IGI Global Scientific Publishing.
[2] Arockia, V. J., Vettriselvan, R., Rajesh, D., Velmurugan, P. R. R., & Cheelo, C. (2025). Leveraging AI and learning analytics for enhanced distance learning. In AI and learning analytics in distance learning (pp. 179–206). IGI Global Scientific Publishing.
[3] Ashifa, K. M. (2019). Developmental initiatives for persons with disabilities. Indian Journal of Public Health Research & Development, 10(12), 1257–1261.
[4] Ashifa, K. M. (2020a). Effect of substance abuse on physical health of adolescents. European Journal of Molecular & Clinical Medicine, 7(2), 3155–3160.
[5] Ashifa, K. M. (2020b). Physical health hazards of schizophrenia patients. Systematic Reviews in Pharmacy, 11(12), 1848–1850.
[6] Ashifa, K. M. (2021a). Analysis on the determinants of health status among tribal communities. Journal of Cardiovascular Disease Research, 12(3), 531–534.
[7] Ashifa, K. M. (2021b). Health status of primitive tribal women in India. Journal of Cardiovascular Disease Research, 12(5), 772.
[8] Ashifa, K. M. (2022). A situation analysis of the social well-being of elderly during the COVID-19 pandemic. International Journal of Health Sciences, 6(3), 10156–10163.
[9] Ashifa, K. M., & Ramya, P. (2019). Health afflictions and quality of work life among women working in fireworks industry. International Journal of Engineering and Advanced Technology, 8(6S3), 1723–1725.
[10] Basha, R., Pathak, P., Sudha, M., Soumya, K. V., & Arockia Venice, J. (2025). Optimization of quantum dilated convolutional neural networks: Image recognition with quantum computing. Internet Technology Letters, 8(3), e70027.
[11] Devi, M., Manokaran, D., Sehgal, R. K., Shariff, S. A., & Vettriselvan, R. (2025). Precision medicine, personalized treatment, and network-driven innovations. In AI for large scale communication networks (pp. 303–322). IGI Global.
[12] Elkin, N., Mohammed, A. K., Kılınçel, Ş., Soydan, A. M., Tanrıver, S. Ç., Çelik, Ş., & Ranganathan, M. (2025). Mental health literacy and happiness among university students. Frontiers in Psychiatry, 16, 1541316.
[13] Gayathri, R. K., Vettriselvan, R., Rajesh, D., Balakrishnan, R., Kumar, R., & Kavitha, J. (2025a). Striking a balance: Mental health challenges and work-life integration among women faculty in Indian B-Schools. Texila International J. of Public Health, 13(2).
[14] Gayathri, R. K., Vettriselvan, R., Rajesh, D., Balakrishnan, R., Kumar, R., & Kavitha, J. (2025b). Strategic role of human resource management in enhancing occupational health and safety practices. Texila International Journal of Public Health, 13(2).
[15] Jenifer, R. D., Vettriselvan, R., Saxena, D., Velmurugan, P. R., & Balakrishnan, A. (2025). Green marketing in healthcare advertising: A global perspective. In AI impacts on branded entertainment and advertising (pp. 303–326). IGI Global.
[16] A S Aneeshkumar, C Jothi Venkateswaran, Reverse sequential covering algorithm for medical Data mining, Procedia Computer Science, Elsevier, 47, pp.109-117.
[17] Kariveliparambil, A., Rasi, R. A., Ahmad, M. S., Öztaş, N., & Ayan, F. S. (2026a). Evolving social capital in indigenous communities. Journal of Social Service Research, 52(1), 147–166.
[18] Kariveliparambil, A., R A, R., Ahmad, M. S., Ramesh, S., & Kuriakose, A. (2026b). Invisible burdens of platform work. International Journal of Qualitative Studies on Health and Well-Being, 21(1).
[19] Kombo, D. K., & Tromp, D. L. A. (2014). Proposal and thesis writing: An introduction. Paulines Publications Africa.
[20] Meena, G., Vettriselvan, R., Rajesh, D., & Velmurugan, P. R. (2025). Diversity and inclusion: Harnessing the power of inclusivity for business success. In Security and strategy models for key-solving institutional frameworks (pp. 203–234). IGI Global Scientific Publishing.
[21] Mohanbabu, S., & Vettriselvan, R. (2025a). Focusing supply chain and container terminal challenges. International Journal of Procurement Management, 24(1), 92–114.
[22] Mohanbabu, S., & Vettriselvan, R. (2025b). Will machine learning resolve the issues in container management. International Journal of Process Management and Benchmarking, 20(4), 559–575.
[23] Orodho, J. A., & Kombo, D. K. (2012). Research methods. Kenyatta University Press.
[24] Rajeswari, M., Rohini, V., Sathya Aarthi, R., Rameshkumaar, V. P., & Arul Krishnan, S. (2026). Blockchain 2.0 for secure, transparent, and autonomous logistics systems. In R. Vettriselvan & N. Suresh (Eds.), Intelligent motion control for human-centered systems (pp. 233–258). IGI Global Scientific Publishing.
[25] Ranganathan, M., Jacob, A., Ashifa, K. M., Kumar, G. J., Anthony, M., Vijay, M., & Kumari, R. B. (2024). An investigation of the effects of chronic stress on attention in parents of children with neurodevelopmental disorders. Universal Journal of Public Health, 12(1), 37–50.
[26] Rasi, R. A., & Ashifa, K. M. (2019). Role of community-based programmes for active ageing. Indian Journal of Public Health Research & Development, 10(12).
[27] Shanthi, H. J., Gokulakrishnan, A., Sharma, S., Deepika, R., & Swadhi, R. (2025). Leveraging artificial intelligence for enhancing urban health. In Nexus of AI, climatology, and urbanism for smart cities (pp. 275–306). IGI Global.
[28] Swadhi, R., Gayathri, K., Suresh, N. V., Catherine, S., & Velmurugan, P. R. (2025a). Leveraging machine learning for enhanced patient engagement and outcomes. In Impact of digital transformation on business growth and performance (pp. 313–340). IGI Global Scientific Publishing.
[29] Swadhi, R., Velmurugan, P. R., Gayathri, K., & Catherine, S. (2025b). Evolving critical themes in advanced human resource management. In Critical aspects in advanced human resource management (pp. 75–102). IGI Global Scientific Publishing.
[30] Vasantha, S., Swadhi, R., Gayathri, K., Selvalakshmi, V., & UmaDevi, A. (2025). Fostering personalized learning and achieving equity in education. In Transforming education with AI-powered personalized learning (pp. 201–236). IGI Global Scientific Publishing.
[31] Venice, J. A., Arivazhagan, D., Suman, N., Shanthi, H. J., & Swadhi, R. (2025a). Recommendation systems and content personalization. In AI for large scale communication networks (pp. 323–348). IGI Global Scientific Publishing.
[32] Venice, J. A., Vettriselvan, R., Jain, S., Madusudanan, K., & Aarthy, C. C. J. (2025b). Performance evaluation and metrics in blockchain powered AI/ML. In Transforming education with AI-powered personalized learning (pp. 143–178). IGI Global Scientific Publishing.
[33] Venice, J. A., Vettriselvan, R., Rajesh, D., Suresh, N. V., & Abirami, P. (2025c). Enabling personalized learning and adaptive systems through strategic management. In Bridging academia and industry through cloud integration in education (pp. 49–72). IGI Global Scientific Publishing.
[34] Venice, J. A., Vettriselvan, R., Rajesh, D., Xavier, P., & Shanthi, H. J. (2025d). Optimizing performance metrics in blockchain-enabled AI/ML data analytics. In Enhancing automated decision-making through AI (pp. 97–122). IGI Global.
[35] Venice, J. A., Sripathi, S. K., & Moonga, B. (2025e). Social deviance and the influence of internet exposure. ASET Journal of Management Science, 4(SI-1).
[36] Venice, J. A. A., Jio, W., Kant, S., Sharda, S., & Mittal, S. (2025f). Ethical leadership effect on the regulation of AI in cyber security. In Ethical challenges of AI and warfare (pp. 133–152). IGI Global Scientific Publishing.
[37] Venice, J. A. A., Muthuraman, M., Kant, S., & Mittal, S. (2026). Community engagement effect on school leadership through digital volunteerism. In Strengthening community engagement and school leadership through digital volunteerism (pp. 85–114). IGI Global Scientific Publishing.
[38] Vettriselvan, R. (2025). Harnessing innovation and digital marketing in the era of industry 5.0. In The future of small business in industry 5.0 (pp. 163–186). IGI Global.
[39] Vettriselvan, R., & Anto, M. R. (2018). Pathetic health status and working condition of Zambian women. Indian Journal of Public Health Research & Development, 9(9), 259–264.
[40] Vettriselvan, R., & Rajan FSA, A. J. (2019). Occupational health issues faced by women in spinners. Indian Journal of Public Health Research & Development, 10(1).
[41] Vettriselvan, R., Deepan, A., Jaiswani, G., Balakrishnan, A., & Sakthivel, R. (2025a). Health consequences of early marriage. In Social, political, and health implications of early marriage (pp. 189–212). IGI Global.
[42] Vettriselvan, R., Velmurugan, P. R., Varshney, K. R., EP, J., & Deepika, R. (2025b). Health impacts of smartphone and internet addictions across age groups. In Impacts of digital technologies across generations (pp. 187–210). IGI Global.
[43] Vettriselvan, R., Velmurugan, P. R., Suresh, N. V., & Catherine, S. (2025c). Strategies, best practices, and pitfalls in the era of digital transformation. In Impact of digital transformation on business growth and performance (pp. 67–98). IGI Global Scientific Publishing.
[44] Vettriselvan, R., Selvi, K., Kumar, A. S., Ranjani, R. D., & Varshney, K. R. (2025d). Ranking methodologies: Criteria and controversies in global higher education. In Global university ranking systems (pp. 109–140). IGI Global Scientific Publishing.
[45] Vettriselvan, R., Gokuldas, P. G., & Sambamoorthy, N. (2025e). Designing language materials to motivate, engage, and empower learners. In Exploring the psychology of language materials development (pp. 279–302). IGI Global Scientific Publishing.
[46] Vettriselvan, R., Ramya, R., Selvalakshmi, V., Jyothi, P., & Velmurugan, P. R. (2026a). Empowering patients through knowledge: Educational strategies in rehabilitation. In Holistic approaches to health recovery (pp. 263–290). IGI Global Scientific Publishing.
[47] Vettriselvan, R., Velmurugan, P. R., Savariapitchai, M., & Swadhi, R. (2026b). AI and international volunteering. In Impacts of AI on international volunteering (pp. 1–24). IGI Global Scientific Publishing.
[48] Vijayalakshmi, M., Subramani, A. K., Vettriselvan, R., Catherin, T. C., & Deepika, R. (2025a). Sustainability and responsibility in the digital era. In Digital citizenship and building a responsible online presence (pp. 285–306). IGI Global.
[49] Vijayalakshmi, M., Subramani, A. K., Vettriselvan, R., Velmurugan, P. R., & Hasine, J. (2025b). Strategic collaborations in medical innovation and AI-driven globalization. In Navigating strategic partnerships for sustainable startup growth (pp. 85–110). IGI Global.
[50] Vinodh, N., Subramani, A. K., & Vettriselvan, R. (2026a). Navigating ethics, society, and governance in the digital age. In Ethics, justice, and governance in the age of AI and digital societies (pp. 1–26). IGI Global Scientific Publishing.
[51] Vinodh, N., Subramani, A. K., & Vettriselvan, R. (2026b). Transforming the future of management and medical education. In AI education strategies for future-proofing curriculum design (pp. 459–476). IGI Global Scientific Publishing.
[52] Zahoor, H., Mustafa, N., Ashifa, K. M., Safaei, M., & El Gamil, R. (2025). Unlocking resilience: Emotional intelligence and self-leadership shape stress perception among health students. International Journal of Innovation and Learning, 38(4), 395–419.

Received : 19 February 2026
Accepted : 26 April 2026
Published : 30 April 2026
DOI: 10.30726/esij/v13.i2.2026.1320017

17.-24-Nasilele-Lubinda.pdf

Effect of Classroom Size on the Academic Performance of Primary School Pupils in Zambia: Evidence from Western Province and Implications for AI-Assisted Large-Class Pedagogy

Author
Nabyana Nabyana, Dr. Ravibaskar Ramalingam
Keywords
Classroom Size; Academic Performance; Primary Education; Western Province; Zambia; AI Large-Class Pedagogy; Digital Instruction; Class Size Reduction.
Abstract
Classroom size the number of learners assigned to a single teacher in a defined instructional setting is a fundamental structural variable with well-documented effects on the quality of teacher-learner interaction, instructional differentiation, formative assessment, and ultimately learner academic performance. In Zambia, primary school classrooms in rural Western Province frequently exceed 60–80 pupils per teacher conditions far beyond pedagogically optimal ratios that constrain the quality and individualisation of instruction available to each learner. This article examines the effect of classroom size on the academic performance of primary school pupils in Western Province, Zambia, situating local findings within global scholarship on class size and learning, AI-assisted large-class pedagogy, digital differentiated instruction, and educational resource optimisation. Drawing on a descriptive survey comparing performance outcomes in schools with different class size conditions, findings confirm significant negative associations between oversized classrooms and pupil academic performance across literacy, numeracy, and science. The article argues that AI-powered classroom management tools, adaptive learning platforms, and digital formative assessment systems offer promising pathways for mitigating large-class instructional quality deficits while infrastructure expansion proceeds. Policy recommendations are presented.
References
[1] Akila, V., Prabhu, G., Akila, R., & Swadhi, R. (2025). Performance metrics in blockchain-enabled AIML for cognitive IoT in large-scale networks. In AI for large scale communication networks (pp. 265–288). IGI Global Scientific Publishing.
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Received : 19 February 2026
Accepted : 25 April 2026
Published : 30 April 2026
DOI: 10.30726/esij/v13.i2.2026.1320016

16.-21-Nabyana-Nabyana.pdf

AI-Driven Adaptive Fraud Detection Framework for Secure Online Financial Transactions

Author
M. Sadiq Valli Khan, Mrs. B. Shireesha
Keywords
Fraud Detection; Random Forest; Machine Learning; Django; Online Transactions.
Abstract
Online financial fraud continues to impose substantial operational and reputational burdens on digital banking and e-commerce platforms. This manuscript presents an AI-driven fraud detection framework that combines a Random Forest ensemble classifier with a Django-based web application for real-time transaction screening. The proposed system evaluates behavioral and contextual signals, including transaction amount, temporal occurrence, geographic deviation from the registered profile, device novelty, location novelty, and transaction type, to estimate fraud risk. A synthetic dataset containing 10,000 transactions is generated to emulate realistic fraud patterns and to support controlled model training and validation. Experimental analysis indicates classification accuracy above 90%, strong precision–recall trade-offs, and an ROC-AUC exceeding 0.92 on held-out samples. The system further exposes feature importance values to support transparent administrative review and auditability. The resulting framework demonstrates that interpretable ensemble learning can deliver scalable, cost-effective, and deployment-ready fraud analytics for modern online transaction environments.
References
[1] L. Breiman, “Random Forests,” Machine Learning, Vol. 45, No. 1, 2001, pp. 5–32.
[2] A. Dal Pozzolo, O. Caelen, R. A. Johnson, and G. Bontempi, “Calibrating Probability with Undersampling for Unbalanced Classification,” IEEE Symposium Series on Computational Intelligence, 2015, pp. 159–166.
[3] S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data Mining for Credit Card Fraud: A Comparative Study,” Decision Support Systems, Vol. 50, No. 3, 2011, pp. 602–613.
[4] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” ACM SIGKDD, 2016, pp. 785–794.
[5] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, Vol. 16, 2002, pp. 321–357.
[6] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, Vol. 12, 2011, pp. 2825–2830.
[7] A. D. Pozzolo, G. Boracchi, O. Caelen, C. Alippi, and G. Bontempi, “Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, No. 8, 2018, pp. 3784–3797.


Received : 07 February 2026
Accepted : 17 April 2026
Published : 21 April 2026

DOI: 10.30726/esij/v13.i2.2026.1320015

Secure-Online-Financial-Transactions-ESIJ.13.2.15.pdf

Voyager AI: An Intelligent Content-Based Travel Recommendation System using TF-IDF Vectorization and Cosine Similarity on a Django Full-Stack Architecture

Author
E. Sandhya, Dr. S. Usharani
Keywords
Content-Based Filtering; TF-IDF Vectorization; Cosine Similarity; Django Web Framework; Personalized Recommendation System.
Abstract
The proliferation of digital travel platforms has generated immense volumes of destination data, yet existing systems remain inadequate in addressing the nuanced personalization requirements of individual travelers. Generic popularity-based rankings and rigid categorical filters fail to capture the semantic richness of individual travel preferences, thereby constituting a significant unresolved challenge in intelligent information retrieval and personalized recommender system design. This paper presents Voyager AI, a full-stack intelligent travel recommendation system engineered to bridge the gap between unstructured user preference expressions and algorithmically curated destination discovery. The proposed system is implemented using the Python-Django 5.x web framework integrated with a content-based filtering engine built upon Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and cosine similarity computation via the scikit-learn library. The architecture encompasses three principal layers: a relational data layer modelled through Django’s Object-Relational Mapper (ORM) managing Destination, User Preference, and Itinerary entities; a machine learning inference layer that transforms free-form natural language preference descriptions and structured categorical inputs into dense numerical vectors and ranks destinations by semantic alignment; and a responsive presentation layer constructed with Bootstrap 5 and custom CSS implementing a glass morphism design paradigm. A key advantage of the content-based approach is its immunity to the cold-start problem, enabling meaningful personalization from the user’s inaugural session without requiring historical interaction data. The system additionally incorporates a budget-aware post-filtering stage and a complete itinerary management module. Experimental validation using a curated multi-category destination dataset demonstrates accurate semantic retrieval across beach, mountain, historical, adventure, and nature destination categories. The findings establish that open-source NLP techniques, when rigorously integrated within a full-stack web architecture, can deliver recommendation quality commensurate with commercial travel intelligence platforms, whilst remaining computationally accessible for independent deployment.
References
[1] C. C. Aggarwal, Recommender Systems: The Textbook, Springer International Publishing, 2016.
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[3] M. Barreda-Angeles and M. Mateus, “Adoption of Artificial Intelligence in the Travel Industry: An Extended Technology Acceptance Model,” Tourism Management Perspectives, Vol. 37, 2021, p. 100789.
[4] R. Burke, “Hybrid Recommender Systems: Survey and Experiments,” User Modeling and User-Adapted Interaction, Vol. 12, Issue 4, 2002, pp. 331–370.
[5] Django Software Foundation, Django Documentation, Version 5.0, 2024. Available: https://docs.djangoproject.com/en/5.0/
[6] A. Holovaty and J. Kaplan-Moss, The Definitive Guide to Django: Web Development Done Right, 2nd ed., Apress, 2009.
[7] P. Lops, M. de Gemmis, and G. Semeraro, “Content-Based Recommender Systems: State of the Art and Trends,” in Recommender Systems Handbook, Springer, 2011, pp. 73–105.
[8] C. D. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval, Cambridge University Press, 2008.
[9] W. McKinney, “Data Structures for Statistical Computing in Python,” Proceedings of the 9th Python in Science Conference, 2010, pp. 51–56.
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Received : 07 February 2026
Accepted : 15 April 2026
Published : 21 April 2026

DOI: 10.30726/esij/v13.i2.2026.1320014

Voyager-AI-ESIJ.13.2.14.pdf

AI-Powered News Recommendation System Using Generative AI and Natural Language Processing

Author
Pallapu Ravi Teja, Mrs. B. Shireesha
Keywords
Generative AI; Natural Language Processing; News Recommendation System; Semantic Search; Personalization; Django; Conversational Interface.
Abstract
The rapid growth of digital news content has created significant challenges in information discovery, leading to issues such as information overload and reduced user engagement. Traditional news recommendation systems rely on keyword-based search, manual categorization, or popularity-driven approaches, which often fail to accurately capture user intent and deliver personalized content. To address these limitations, this paper proposes an AI-Powered News Recommendation System that utilizes Generative AI and Natural Language Processing (NLP) to provide context-aware and user-centric recommendations. The system integrates Google’s Gemini generative AI model within a Django-based web application, enabling users to interact through natural language queries via a conversational interface. The generative AI model interprets user intent, extracts semantic meaning, and maps queries to relevant news categories, allowing efficient retrieval of articles from a structured database. This approach effectively bridges the gap between unstructured user input and structured data storage, thereby improving recommendation accuracy and relevance. The system follows a three-tier architecture consisting of the presentation layer, application layer, and data layer. The backend is implemented using Python and Django, while the frontend is developed using HTML, CSS, JavaScript, and Bootstrap to ensure a responsive user experience. Experimental results demonstrate that the proposed system reduces information overload, improves recommendation precision, and enhances overall user satisfaction. The integration of generative AI enables flexible, intelligent, and context-driven query processing, making the system more effective than conventional approaches.
References
[1] C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. Cambridge University Press, 2008.
[2] T. Brown et al., “Language models are few-shot learners,” Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901, 2020.
[3] M. Pazzani and D. Billsus, “Content-based recommendation systems,” in The Adaptive Web, Springer, 2007, pp. 325–341.
[4] P. Resnick and H. R. Varian, “Recommender systems,” Communications of the ACM, vol. 40, no. 3, pp. 56–58, 1997.
[5] P. Lops, M. de Gemmis, and G. Semeraro, “Content-based recommender systems: State of the art and trends,” in Recommender Systems Handbook, Springer, 2011, pp. 73–105.
[6] A. Ricci, L. Rokach, and B. Shapira, Recommender Systems Handbook, 2nd ed. Springer, 2015.
[7] J. Devlin et al., “BERT: Pre-training of deep bidirectional transformers for language understanding,” Proceedings of NAACL-HLT, pp. 4171–4186, 2019.
[8] A. Vaswani et al., “Attention is all you need,” Advances in Neural Information Processing Systems, 2017.
[9] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
[10] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.
[11] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Morgan Kaufmann, 2011.
[12] A. Holovaty and J. Kaplan-Moss, The Definitive Guide to Django. Apress, 2009.
[13] S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. O’Reilly Media, 2009.
[14] D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed., Pearson, 2020.
[15] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed. Pearson, 2010.


Received : 07 February 2026
Accepted : 15 April 2026
Published : 21 April 2026

DOI: 10.30726/esij/v13.i2.2026.1320012

AI-Powered-News-Recommendation-ESIJ.13.2.12.pdf

AI-Driven Smart Attendance and Academic Performance Prediction System Using Machine Learning

Author
Gutti Reddy Prakash, Mrs. V. Vijayalakshmi
Keywords
Artificial Intelligence; Attendance Management; Machine Learning; Random Forest; Student Performance Prediction.
Abstract
The increasing demand for intelligent academic management systems has accelerated the integration of artificial intelligence and machine learning techniques into educational environments. Traditional attendance management systems, which rely on manual processes or basic digital tools, are inefficient, error-prone, and lack the analytical capabilities required for proactive academic monitoring. This paper presents an AI-Driven Smart Attendance and Academic Performance Prediction System, designed to automate attendance tracking while integrating predictive analytics for enhanced decision-making. The proposed system is developed using Python and the Django web framework, with SQLite as the backend database and Scikit-learn for implementing machine learning algorithms. A Random Forest Classifier is employed to analyse student data, including attendance percentage, assignment scores, quiz performance, late submissions, and study hours. Based on these features, the system predicts whether a student is academically safe or at risk, enabling early intervention by educators. The application follows a modular architecture consisting of student management, attendance tracking, AI prediction, reporting and visualisation, and dashboard modules. Interactive dashboards and graphical reports are implemented using Chart.js to provide real-time insights into student performance. Experimental results demonstrate that the system effectively identifies at-risk students and supports data-driven academic decisions. This work contributes toward transforming conventional attendance systems into intelligent academic monitoring platforms aligned with modern educational technology trends [1][2].
References
[1] C. Romero and S. Ventura, “Educational data mining: A survey from 1995 to 2005,” Expert Systems with Applications, vol. 33, no. 1, pp. 135–146, 2007.
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[3] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
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[5] A. Holovaty and J. Kaplan-Moss, The Definitive Guide to Django: Web Development Done Right. Apress, 2009.
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[7] T. Almarabeh, H. Amer, and A. Al-Badarneh, “Students’ perceptions of E-learning at the University of Jordan,” International Journal of Emerging Technologies in Learning, vol. 11, no. 4, pp. 31–35, 2016.
[8] I. Sommerville, Software Engineering, 10th ed. Pearson, 2015.
[9] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Morgan Kaufmann, 2011.
[10] S. Few, Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, Analytics Press, 2013.
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[12] M. Fowler, Patterns of Enterprise Application Architecture, Addison-Wesley, 2002
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[14] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, Springer, 2013.
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[16] D. Abadi et al., “The design of the Borealis stream processing engine,” CIDR Conference, 2005.
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Received : 07 February 2026
Accepted : 15 April 2026
Published : 21 April 2026

DOI: 10.30726/esij/v13.i2.2026.1320013

AI-Driven-Smart-Attendance-ESIJ.13.2.13.pdf

Beyond Desalination: Convergent Renewable–Biotechnological Approaches to Desert Water Security

Author
Rohit Gupta, Rahul Gupta, Rajnish Magotra
Keywords
Water Scarcity; Deserts; Technological Innovations; Atmospheric Water Harvesting; Solar Desalination; Fog Collection; Aquifer Recharge; Biotechnological Solutions.
Abstract
Water scarcity remains one of the major problems in arid and semi-arid areas, especially in deserts where there is almost no source of conventional fresh water. The need for dependable and environmentally friendly water sources in desert areas is becoming more and more pressing as climate change worsens and populations increase. This research paper delves into the latest scientific breakthroughs and technological innovations that aim to make water available in deserts. It looks into the methods of atmospheric water harvesting, solar desalination, fog collection, aquifer recharge, and biotechnological solutions such as water-generating nanomaterials and genetically engineered drought-tolerant plants. Considerable emphasis is put on the use of renewable energy systems, especially solar and wind, for providing energy to water production processes in a sustainable way. The paper also presents and evaluates the implementations of the technologies in the real world through the case studies of Israel, the United Arab Emirates, and Chile. Identifying environmental repercussions along with technical feasibility, this research intends to be a thorough introductory account of how science and technology can make the water supply system accessible in the harshest water-stressed areas of the earth.
References
[1] Kim, H., Rao, S. R., Furukawa, H., et al. (2019). Water harvesting from air with metal-organic frameworks powered by natural sunlight. Science, 356(6336), 430-434. https://doi.org/10.1126/science.aam8743
[2] Miller, S. A., Becerra, S., & Thompson, M. (2021). Atmospheric water harvesting: Advances and challenges for practical applications. Renewable and Sustainable Energy Reviews, 145, 111074. https://doi.org/10.1016/j.rser.2021.111074
[3] Elimelech, M., & Phillip, W. A. (2011). The future of seawater desalination: Energy, technology, and the environment. Science, 333(6043), 712–717. https://doi.org/10.1126/science.1200488
[4] Alkhudhiri, A., Darwish, N., & Hilal, N. (2012). Membrane distillation: A comprehensive review. Desalination, 287, 2-18. https://doi.org/10.1016/j.desal.2011.08.027
[5] Schemenauer, R. S., & Cereceda, P. (1994). Fog water collection in arid coastal locations. Ambio, 23(7), 342–347. https://doi.org/10.2307/4313817
[6] Park, K.-C., Kim, H., Lee, D., et al. (2020). Nanostructured surfaces for enhanced fog harvesting. Nature Communications, 11, 1945. https://doi.org/10.1038/s41467-020-15791-4
[7] Scanlon, B. R., Reedy, R. C., Stonestrom, D. A., et al. (2006). Global synthesis of groundwater recharge in semiarid and arid regions. Hydrological Processes, 20(15), 3335–3370. https://doi.org/10.1002/hyp.6335
[8] Rodell, M., Velicogna, I., & Famiglietti, J. S. (2009). Satellite-based estimates of groundwater depletion in India. Nature, 460(7258), 999–1002. https://doi.org/10.1038/nature08238
[9] Panagopoulos, A., Katsaprakakis, D. A., & Belessiotis, V. (2019). Renewable energy powered desalination-Technology, environmental and economic review. Renewable and Sustainable Energy Reviews, 112, 87–104. https://doi.org/10.1016/j.rser.2019.05.002
[10] Tian, G., Fu, C., & Guo, Z. (2025). A novel hygroscopic salt‑modified MOF‑303 with efficient solar‑driven water harvesting from arid air. Nanoscale, 17(12340‑12349). https://doi.org/10.1039/D5NR00482A
[11] Aziz, M., Hassan, R., Saeed‑Ul‑Hassan, M., Ehtisham, M., Almatawa, M. S., Badawi, A. K., & Ismail, B. (2025). Efficient water capture under low humidity using Ni‑modified MOF‑5: scalable atmospheric water harvesting systems. RSC Advances, 15, 34003‑34015. https://doi.org/10.1039/D5RA05257B
[12] Ahrestani, Z., Sadeghzadeh, S.S., & Motejadded Emrooz, H. B. (2023). An overview of atmospheric water harvesting methods, the inevitable path of the future in water supply. RSC Advances, 13, 10273‑10307. https://doi.org/10.1039/D2RA07733G
[13] Hanikel, N., Prévot, M. S., Yaghi, O. M., et al. (2020). Practical water production from desert air. Science Advances, 6(25), eaat3198. https://doi.org/10.1126/sciadv.aat3198
[14] Ilas, T., van de Boel, P., Poelman, H., & Van Bael, M. K. (2021). Exploiting radiative cooling for uninterrupted 24‑hour water harvesting from the atmosphere. Science Advances, 7(15), eabf3978. https://doi.org/10.1126/sciadv.abf3978
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Received : 16 January 2026
Accepted : 15 April 2026
Published : 18 April 2026

DOI: 10.30726/esij/v13.i2.2026.1320011

Desert-Water-Security-ESIJ.13.2.11.pdf

Design and Fabrication of a Smart Automotive Air Conditioning System for Passenger Cars with Adaptive Thermal Control

Author
Amol More
Keywords
Automotive Air Conditioning; Smart HVAC; Refrigerant Cycle; Thermal Management; Passenger Comfort; Energy Efficiency.
Abstract
Passenger comfort and energy efficiency are critical performance parameters in modern automobiles, especially under varying climatic and operating conditions. Conventional automotive air conditioning (AC) systems operate at fixed or manually controlled settings, often resulting in excessive energy consumption and non-optimal cabin comfort. This study presents the design, fabrication, and implementation of a smart air conditioning system for passenger cars based on thermodynamic optimization and adaptive control. The system integrates core refrigeration components including compressor, condenser, expansion valve, evaporator, and receiver dryer with intelligent sensor-based control for temperature, pressure, and cabin load variations. The proposed system dynamically regulates refrigerant flow and compressor operation using real-time feedback to maintain optimal thermal comfort while minimizing energy consumption. Component selection, hose fabrication, system assembly, vacuum testing, and refrigerant charging procedures are detailed. Comparative analysis between conventional and smart control modes demonstrates improved cooling response, reduced compressor cycling losses, and enhanced system reliability. The developed smart AC system provides an effective solution for customized vehicle applications, vintage restorations, and energy-efficient automotive thermal management.
References
[1] J. H. Kim, S. Park, and K. Lee, “Energy-efficient control strategy for automotive air conditioning systems,” Applied Thermal Engineering, vol. 148, pp. 123–131, 2019.
[2] S. Wang and Y. Chen, “Intelligent vehicle cabin thermal management using sensor-based adaptive control,” Energy Conversion and Management, vol. 210, 2020, Art. no. 112735.
[3] SAE International, Automotive Air Conditioning System Design Manual. Warrendale, PA, USA: SAE, 2018.
[4] ASHRAE Handbook—HVAC Applications. Atlanta, GA, USA: ASHRAE, 2021.
[5] K. Lee, M. Choi, and H. Kim, “Energy optimization of HVAC systems for electric vehicles using adaptive compressor control,” Applied Energy, vol. 285, 2021, Art. no. 116432.
[6] R. Patel and D. Shah, “Performance analysis of retrofit automotive air conditioning systems,” International Journal of Automotive Technology, vol. 23, no. 4, pp. 987–995, 2022.
[7] A. R. Abu-Mulaweh, “Energy analysis of automotive air conditioning systems,” International Journal of Energy Research, vol. 35, no. 12, pp. 1041–1048, 2011.
[8] S. K. Wang, Handbook of Air Conditioning and Refrigeration, 2nd ed. New York, NY, USA: McGraw-Hill, 2017.
[9] H. He, J. Peng, and H. Xiong, “Thermal management system optimization for electric vehicles under different driving conditions,” Applied Thermal Engineering, vol. 102, pp. 449–458, 2016.
[10] M. Fayazbakhsh and M. Bahrami, “Comprehensive review of vehicle cabin thermal comfort and HVAC systems,” Applied Thermal Engineering, vol. 89, pp. 123–132, 2015.
[11] J. Rugh and J. Farrington, “Vehicle ancillary load reduction project: A review of thermal load reduction techniques,” National Renewable Energy Laboratory (NREL), USA, Tech. Rep., 2008.

Received : 19 February 2026
Accepted : 14 April 2026
Published : 17 April 2026

DOI: 10.30726/esij/v13.i2.2026.1320010

Smart-Automotive-Air-Conditioning-ESIJ.13.2.10.pdf

Improvement of Cyclohexane Recovery Unit in a Polyethylene Plant: Process Optimization and Economic Evaluation

Author
Steven Oyalemi, Otaraku, I. Jonathan, Akuma A. Oji
Keywords
Cyclohexane Recovery; Process Optimization; Polyethylene Production; Solvent Recycling; Aspen HYSYS Simulation; Heat Integration.
Abstract
This study presents a comprehensive approach to improving Cyclohexane recovery in a polyethylene production plant through process optimization and equipment modification. The current solvent recovery system, which utilizes three shell-and-tube heat exchangers in series, achieves only 83% recovery efficiency, leading to significant economic losses and environmental concerns. Cyclohexane is an indispensable solvent in the solution phase polymerization process critical for the manufacturing of diverse polyethylene products, including high-density (HDPE), low-density (LDPE), and linear low-density polyethylene (LLDPE). Despite its pivotal role, industrial operations frequently encounter substantial challenges in achieving comprehensive solvent recovery, leading to considerable economic penalties and an escalation in overall production costs. This comprehensive study undertakes an in-depth investigation into advanced methodologies aimed at enhancing cyclohexane recovery within an established polyethylene production facility. The research employs sophisticated process simulation techniques, specifically utilizing Aspen HYSYS, to model and analyze proposed modifications to the existing solvent recovery unit. A central focus is placed on assessing the impact of integrating a new condenser, referred to as a “chiller,” and meticulously examining how variations in operating temperatures influence both solvent recovery efficiency and the energy duty required by the chiller.

The empirical findings from the simulation rigorously demonstrate a pronounced inverse correlation between decreasing operating temperature and an increase in the volume of cyclohexane recovered. Concomitantly, this enhanced recovery at lower temperatures is directly linked to a significant rise in the energy duty demanded by the chiller. While the initial capital investment associated with the installation of these chillers is substantial, estimated at approximately $746,900.735, the projected economic analysis reveals a highly favorable and short payback period of approximately 1.57 years. This rapid return on investment strongly underscores the economic viability and strategic benefits of implementing such process enhancements. Ultimately, this study furnishes a detailed blueprint of simulated optimal operating conditions for superior cyclohexane recovery, thereby offering invaluable practical insights and a robust framework for the design, development, and optimized operation of future and existing polyethylene manufacturing facilities.

References
[1] Aspen Technology. (2012). Aspen HYSYS User Guide. Burlington, MA: Aspen Technology Inc.
[2] Baeyer, A. (1870). Ueber die Reduction aromatischer Kohlenwasserstoffe durch Jodphosphonium. Annalen der Chemie und Pharmacie, 155, 266–281.
[3] Bates, F. S., & Fredrickson, G. H. (2014). Block Copolymer Thermodynamics: Theory and Experiment. Annual Review of Physical Chemistry, 41, 525–557.
[4] Campbell, M. L. (2011). Cyclohexane. Ullmann’s Encyclopedia of Industrial Chemistry.
[5] Chanda, M. (2013). Introduction to Polymer Science and Chemistry (2nd ed.). CRC Press.
[6] Chremos, A., Nikoubashman, A., & Panagiotopoulos, A. (2014). Flory-Huggins parameter, from binary mixtures of Lennard-Jones particles to block copolymer melts. Journal of Chemical Physics.
[7] Eisenbach, C. D., & Heinemann, T. (1995). Thermoplastic graft copolymer elastomers with chain-folding or bifurcated side chains. Macromolecular Chemistry and Physics, 196(8), 2669–2686.
[8] Zhang, F. F., van Rijnman, T., Kim, J. S., & Cheng, A. (2008). On Present Methods of Hydrogenation of Aromatic Compounds, 1945 to Present Day. Lunds Tekniska Högskola.
[9] Gazit, O., Khalfin, R., Cohen, Y., & Tannenbaum, R. (2009). Self-assembled diblock copolymer ”nanoreactors” as catalysts for metal nanoparticle synthesis. Journal of Physical Chemistry C, 113, 576–583.
[10] Hadjichristidis, N., Pispas, S., & Floudas, G. (2003). Block Copolymers: Synthetic Strategies, Physical Properties, and Applications. Wiley.
[11] Hamley, I. W. (1998). The Physics of Block Copolymers. Oxford University Press.
[12] Hamley, I. W. (2004). Developments in Block Copolymer Science and Technology. Wiley.
[13] Mayer, J., Urban, S., Habrylo, S., Holderna, K., Natkaniec, I., Würflinger, A., & Zajac, W. (1991). Neutron Scattering Studies of C6H12 and C6D12 Cyclohexane under High Pressure. Physica Status Solidi (b), 166(2), 381.
[14] McNaught, A. D., & Wilkinson, A. (1996). Glossary of basic terms in polymer science (IUPAC Recommendations 1996). Pure and Applied Chemistry, 68, 2287–2311.
[15] Prince, D. M. (1995). Temperature Calibration of Differential Scanning Calorimeters. Journal of Thermal Analysis, 45(6), 1285–1296.
[16] Warnhoff, E. W. (1996). The Curiously Intertwined Histories of Benzene and Cyclohexane. Journal of Chemical Education, 73(6), 494.
[17] Musser, M. T. (2005). Cyclohexanol and Cyclohexanone. Ullmann’s Encyclopedia of Industrial Chemistry. Wiley-VCH.

Received : 07 July 2025
Accepted : 12 April 2026
Published : 17 April 2026

DOI: 10.30726/esij/v13.i2.2026.132009

Cyclohexane-Recovery-ESIJ.13.2.9.pdf

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