http://repository.unizik.edu.ng/handle/123456789/197 Scholarly Works Wed, 26 Aug 2026 21:17:12 GMT 2026-08-26T21:17:12Z http://repository.unizik.edu.ng/handle/123456789/1282 Title: Review of Oil and Gas Composite Piping System Authors: Ihueze, Christopher Chukwutoo; Chukwumuanya, Emmanuel Okechukwu Abstract: Continued increase in the use of vessels for storage, industrial processing, and powergeneration under unusual conditions of pressure, temperature, and environment has given special emphasis to analytical and environmental methods for determining their operating stresses. Creeps, fracture, fatigue, explosion, vibration, expansion, buckling loads, etc constitute factors known to cause some form of stresses and deformations or failure in composite material vessels used in oil and gas industry. Good knowledge of the nature and magnitude of these stresses enables provision of the adequate methods for their prevention and control. The study aimed at modeling the wall thickness of an oil pipeline (pressurized cylindrical vessels) used in transferring oil from one location to another. In doing so, a review of some published works on such factors as creeps, fracture, fatigue failure, expansion, buckling loads, etc was also made. A liquid flow problem was formulated and solved using a number of the existing design failure theories used in the design of oil and gas piping systems. The solved problem shows vividly how the wall thickness of an oil pipeline under fluid pressure can be determined and selected from among the existing commercial standard pipe sizes. It is hoped that this work will answer some of the questions asked by engineering students during lectures on theories and design of pressure vessels Description: scholarly works Thu, 01 Dec 2016 00:00:00 GMT http://repository.unizik.edu.ng/handle/123456789/1282 2016-12-01T00:00:00Z http://repository.unizik.edu.ng/handle/123456789/1242 Title: Machine Learning Applications for Production Scheduling Optimization Authors: Aguh, Patrick Sunday; Udu, Chukwudi Emeka; Chukwumuanya, Emmanuel Okechukwu; Okpala, Charles Chikwendu Abstract: Production scheduling represents a critical function within manufacturing and industrial operations, exerting a direct influence on productivity, operational efficiency, and overall cost management. Traditional scheduling methodologies, while foundational, often exhibit limitations when confronted with the complexity, variability, and dynamic demands of contemporary production environments. In response, this paper investigates the potential of Machine Learning (ML) techniques for the enhancement of production scheduling outcomes. Specifically, it examines the capabilities of reinforcement learning, neural networks, and genetic algorithms to model complex systems, adapt to real-time disruptions, and support more effective decision-making processes. The paper further reviews notable industrial applications of these techniques, critically evaluating their performance relative to conventional methods. In addition, it addresses the inherent challenges associated with the deployment of ML in production scheduling, including data availability, algorithmic interpretability, and integration with legacy systems. Finally, the study outlines future research directions, emphasizing the need for more robust, scalable, and interpretable ML-based scheduling solutions to meet the evolving demands of modern industry. Description: scholarly works Wed, 01 Jan 2025 00:00:00 GMT http://repository.unizik.edu.ng/handle/123456789/1242 2025-01-01T00:00:00Z http://repository.unizik.edu.ng/handle/123456789/1197 Title: LEAN PRINCIPLES INTEGRATION WITH DIGITAL TECHNOLOGIES: A SYNERGISTIC APPROACH TO MODERN MANUFACTURING Authors: Chukwumuanya, Emmanuel Okechukwu; Udu, Chukwudi Emeka; Okpala, Charles Chikwendu Abstract: The integration of lean principles with digital technologies marks a transformative shift in modern manufacturing and operations management. Lean methodologies focus on reducing waste, optimizing resources, and maximizing value, while digital tools such as IoT, AI, and Big Data Analytics enable real-time monitoring, predictive insights, and automation. This study explores how the combination of these paradigms will enhance operational efficiency, agility, and competitiveness in manufacturing environments. Through an analysis of applications like smart production systems, predictive maintenance, and digital value stream mapping, the research highlights significant benefits, including improved quality, faster decision-making, and reduced downtime. It also examines challenges such as technological complexity, data security, organizational resistance, and the need for workforce upskilling. Emerging trends like Industry 5.0 and human-centric smart factories are discussed, emphasizing the evolving landscape of digitally driven lean manufacturing. The findings demonstrated that integrating lean principles with digital technologies is no longer optional, but essential for firms aiming to thrive in an increasingly dynamic global market. This synergy represents a strategic pathway towards sustainable operational excellence and innovation in the manufacturing sector. Description: scholarly works Wed, 01 Jan 2025 00:00:00 GMT http://repository.unizik.edu.ng/handle/123456789/1197 2025-01-01T00:00:00Z http://repository.unizik.edu.ng/handle/123456789/1195 Title: THEORETICAL DESIGN OF A NON-ENERGY RECOVERY INCINERATOR FOR AWKA MUNICIPALITY Authors: Chukwumuanya, Emmanuel Okechukwu; Ihueze, Christopher Chukwutoo; Chukwuma, Emmanuel Chibundo Abstract: Waste management in Awka, the Capital City of Anambra State, Nigeria is the principal function of the Anambra State Waste Management Agency. Waste disposal practices in the area are mainly by open dumping and open burning which constitute serious health hazards to the residents and the environment. Use of well designed incinerators and well-built landfills are yet to attract some interest in the said area; probably because of the huge costs of equipment procurement and the expertise involved. The study aimed at designing a hypothetical non-energy recovery incinerating system for municipal solid waste generated in Awka Municipality. The incinerator is rated at a capacity of 45 tons/day, with a charging rate of 5.625 tons/hr. The relation between the refuse and the flue gases and the amounts of water and air required for complete combustion of the refuse and the necessary steps taken to ensure emission of clean gas through the stack are presented. Various assumptions were made which informed the design of the facility's combustion chambers. The materials flow/balance analyses are presented. It is hoped that this hypothetical design will provoke some interests that would lead to actual fabrication of the designed municipal solid waste incinerator for Awka urban area of Anambra State Description: Scholarly works Mon, 01 Jan 2018 00:00:00 GMT http://repository.unizik.edu.ng/handle/123456789/1195 2018-01-01T00:00:00Z