Hysteresis Dispersion Compensation with Neural Network based Controller

Amigues, L.; Pommier-Budinger, Valérie; Bordeneuve, J.

doi:10.1016/j.ifacol.2020.12.1513

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Therefore, it is used with non-linear plants in prediction systems presented Mohamed et al [8] or satellite dynamic altitude control systems [9]. Intelligent control applications cover vast and diverse fields from academic settings to industrial processes such as those in [10]- [16]. Neural controllers have even been developed for security management in residential homes [17].…”

Section: Introductionmentioning

confidence: 99%

Neural network-based pH and coagulation adjustment system in water treatment

Mora

Nieto

Ortega

et al. 2023

IJ-AI

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This document presents a machine learning model development as a tool to improve chemical dosing procedure in ariari regional aqueduct (ARA). The supervised learning model has been addressed starting from the knowledge of data color, turbidity and pH at the water inlet to the aqueduct and the dosing results of type A aluminum sulfate and calcium oxide (lime) obtained through jar tests. The construction of the automatic learning model had a comprehensive implementation and improvement field through continuous system training, which allowed an optimal dosage of Aluminum Sulfate and Lime to generate an outlet pH less than 7.5 and outlet turbidity less than 8 nephelometric turbidity unit (NTU). Those outlet water parameters meet the ministry of social protection criteria in Colombia. Also, a virtual jar test was created to reduce the time required to obtain chemical dosing values to less than a minute. In contrast, a laboratory test takes approximately a half-hour to displays results.

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