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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Bright, Glen
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Computer Science & IT Engineering

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Optimization control design and simulation of furnace-fired boiler exit pressure: leveraging disruptive technology Salawu, Ganiyat Abiodun; Bright, Glen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2979-2990

Abstract

The efficient operation of furnace-fired drum boilers is critically dependent on the precise control of downstream exit pressure, especially in the presence of stochastic heat fluctuations. This paper presents a stochastic control approach for regulating the downstream exit pressure in a furnace-fired boiler subject to random heat fluctuations. A stochastic model of the boiler dynamics is developed, incorporating heat transfer and combustion uncertainties. By leveraging disruptive technology, such as the model predictive control (MPC), strategies were designed to optimize the downstream exit pressure in real-time, and minimizing deviations from the set point. Simulation studies demonstrated the effectiveness of the proposed approach in maintaining a stable exit pressure despite random heat fluctuations. Results show significant improvements in boiler performance and efficiency compared to traditional proportional integral derivative (PID) control. The proposed stochastic control strategy offers a promising solution for reliable and efficient operation of furnace-fired boilers under uncertain conditions.
Optimizing robotic motion in dynamic manufacturing environments Abiodun Salawu, Ganiyat; Bright, Glen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4590-4599

Abstract

The field of robotics has been a trending technology over the years due to its ability to revolutionize industries. This study highlights the role of optimized robotic motion in enhancing productivity in dynamic manufacturing environments using MATLAB simulations. By modeling the arrival of manufactured parts in batches via a conveyor system governed by a negative exponential distribution in a Poisson process, MATLAB is employed to design optimal robotic trajectories for pick-and-place operations. The research carefully analyzes parameters such as arrival rates and cycle times to manage the stochastic nature of part delivery. The result reveals a significant improvement in operational efficiency, with throughput increasing by up to 20% due to real-time optimization of robotic motion. The non-linear relationship between throughput and arrival rates highlights the system’s complexity, with optimal conditions observed at specific arrival rates, such as 0.16 s for peak efficiency. MATLAB’s Polynomial Trajectory Planning tool generates smooth, continuous paths, ensuring that robotic operations dynamically adapt to changing conditions. This foundation supports future innovations in robotic system integration and automated production lines, offering a significant step forward in the application of advanced simulation tools an advanced manufacturing environment.
Co-Authors Abiodun Salawu, Ganiyat Salawu, Ganiyat Abiodun