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Okeke, C. A.
Department of Electrical and Electronics Engineering, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria
Ngene, C. O.
Department of Electrical and Electronics Engineering, Enugu State University of Science and Technology, Enugu State, Nigeria
Nwagwu, C.
Production Line Manager, Seven-Up Bottling Company Plc, 9th Mile Corner, Enugu State, Nigeria
ABSTRACT
Ensuring
consistent water quality is critical in beverage production, where deviations
in parameters directly affect the quality of food drinks. Conventional
monitoring and control systems often struggle to handle the multivariate and
interdependent nature of these parameters. In this study, a dataset comprising
pH, chlorine, taste, turbidity, color, hardness, total dissolved solids (TDS),
conductivity, oxygen, temperature, solubility and flow rate was analyzed in
MATLAB using Pearson correlation and average absolute correlation (AAC), and
parameters with |r| ≥ 0.7 were identified as crucial parameters. The resulting
crucial parameters, pH, turbidity, hardness, TDS, conductivity, temperature,
and solubility, were highlighted as the most influential variables for beverage
production control. Consequently, a model predictive control (MPC) strategy
with an 8×8 multi-input multi-output (MIMO) discrete-time plant model was
developed for real-time regulation of the eight selected water parameters.
Simulation results show that the MPC controller successfully regulates the
subjected parameters toward their respective set-points, pH (7.53), turbidity
(1.28NTU), color (5.00TCU), hardness (85.00mg/L), TDS (145.00ppm), conductivity
(465.00µS/cm), temperature (23.75°C) and solubility (402.50 mg/L). The
controlled responses reveals the controller’s ability to attain accurate
tracking set-points and effectively managing multivariate interactions within
the system dynamics.
These findings shows that model predictive control plays important role in
enhancing water quality and providing approach for automated control in
beverage production processes.
Keywords: Beverage Production, Process Control, Water Quality Regulation, Model Predictive Control (MPC), Multivariate Parameters Control
https://doi.org/10.33922/j.ujet_v12i1_6
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View: 17 | Download: 7
Published
Saturday, February 21, 2026
Issue
Vol. 12, No. 1, March 2026
Article Section
GENERAL
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