Smell Agent Optimization Based Supervisory Model Predictive Control for Energy Efficiency Improvement of a Cold Storage System

Bankole, Adesola T.; Moses, Stephen O. Okpanachi; Ibitoye, Tahir Y.

doi:10.1109/nigercon54645.2022.9803096

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…. , N − 1 (13) According to the receding horizon concept, the optimal sequence is gotten over N steps, while the first optimal control action u*(t) is used. New measurements/state estimates are gotten at time t + 1, and the process of optimization is repeated [16].…”

Section: Linear Model Predictive Controllermentioning

confidence: 99%

“…Model predictive controller (MPC) has been used for refrigeration system control in [11]; however, their implementation did not consider the use of optimization algorithms for improvement in system performance and violation of temperature constraints was reported. The smell agent optimization algorithm developed by [12] was used in [13] for a simple thermal system; however, the temperature responses are focused on food preservation and are not within the recommended temperature range required for vaccine preservation. Particle swarm optimization (PSO) for enhancement of refrigeration system control was reported in [14]; however, the system responses are unfit to be used for vaccination purposes.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of compressive strength of high-performance concrete via automated machine learning models

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Enhancing unconfined compressive strength of stabilized soil with lime and cement prediction through a robust hybrid machine learning approach utilizing Naive Bayes Algorithm

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The unconfined compressive strength (UCS) of stabilized soil with lime and cement is a crucial mechanical factor in developing accurate geomechanical models. In the past, determining UCS required laborious laboratory testing of core samples or complex well-log analysis, both of which consumed many resources. This study introduces a novel method for real-time UCS prediction while acknowledging the need for efficiency. This method makes use of Specific Naive Bayes (NB) predictive models that are strengthened by the smell agent optimization (SAO) and the Dynamic Arithmetic Optimization Algorithm (DAOA), two reliable meta-heuristic algorithms. Combining these algorithms improves prediction precision while streamlining the process. By examining UCS samples from various soil types obtained from earlier stabilization tests, these models are validated. This study identifies three different models: NBDA, NBSA, and a single NB. The individual insights each model provides work in concert to increase the overall UCS prediction accuracy. This approach represents a significant advancement in UCS prediction methodologies, revealing a quick and effective method with wide-ranging implications for various geomechanical applications. Meta-heuristic algorithms combined with particular NB models produce promising results, opening up new possibilities for real-time UCS estimation across various geological scenarios. Especially noteworthy are the NBDA model’s impressive performance metrics. The entire dataset achieves an R2 value of 0.992 during testing. The RMSE of 108.69 for the NBDA model during the training phase also shows that it has the best performance overall. It consistently exhibits commendable generalization and predictive abilities that outperform those of the developed NB and NBSA models, highlighting its usefulness and effectiveness in practical applications.

show abstract

Smell Agent Optimization Based Supervisory Model Predictive Control for Energy Efficiency Improvement of a Cold Storage System

Cited by 3 publications

References 11 publications

Aquila-Based Model Predictive Control of an Adapted Fridge Display Case Model for Vaccine Preservation

Aquila-Based Model Predictive Control of an Adapted Fridge Display Case Model for Vaccine Preservation

Prediction of compressive strength of high-performance concrete via automated machine learning models

Enhancing unconfined compressive strength of stabilized soil with lime and cement prediction through a robust hybrid machine learning approach utilizing Naive Bayes Algorithm

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