ADAPTIVE AND PREDICTIVE CONTROL IN NIGERIAN INDUSTRIES: IMPROVING EFFICIENCY AND RESILIENCE

Abstract

Nigeria's industrial sector faces ongoing challenges like fluctuations in raw material quality, aging equipment, power inconsistencies, and a lack of real-time optimization. Traditional control systems, such as PID and manual tuning, often struggle in these conditions, resulting in low productivity, high energy use, and frequent downtime. This article explores how adaptive control (AC) and model predictive control (MPC) can be applied in Nigerian industries, focusing on manufacturing, petrochemicals, food processing, and cement production. By using a mixed-methods approach, including surveys from 30 industrial plants and simulation case studies, the study compares traditional control with AC and MPC based on set point tracking, energy savings, and disturbance rejection. Results indicate that AC and MPC can reduce settling time by 42%, cut energy use by 18-25%, and lower variability in product quality by 35% compared to traditional methods. Challenges to implementation include high startup costs, a shortage of skilled personnel, and inadequate sensor infrastructure. The article recommends a phased adoption strategy, local training programs, and government financial support. The findings offer a practical framework for modernizing Nigerian process control to align with Industry 4.0.