article · Journal of Water Reuse and Desalination
Processing fruits and vegetables produces effluent and wash-waters containing organic matter and particles that require treatment to meet regulatory standards. A new approach combines photovoltaic renewable energy with an oxidation process for saline water analysis, supported by deep learning methods. Saline water analysis is conducted using Markov fuzzy-based Q-radial function neural networks. To support effective monitoring and control of water consumption, the operational framework is structured to be entirely web-oriented. It integrates a dedicated communication system capable of gathering data formatted as irregularly spaced time series. Experimental testing evaluates the system using water salinity data, assessing performance across metrics including accuracy, precision, recall, specificity, computational cost, and the kappa coefficient.
Washing and processing fresh produce generates wastewater that must comply with environmental and safety standards before reuse or disposal. Integrating solar energy with advanced neural network analysis allows automated tracking of water salinity and quality. A web-connected setup handling irregular operational data helps agricultural processors manage water consumption efficiently and keep treatments aligned with regulatory demands.
This technology could support fruit and vegetable processors and wastewater treatment operators seeking automated, renewable-powered water quality monitoring. Its web-oriented architecture and irregular time series data processing suit decentralised agricultural or industrial facilities. Because the abstract details only an experimental evaluation across standard computational and predictive metrics, the system appears to be early-stage research that requires further pilot-scale validation before commercial deployment.
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Abstract The amount of particles and organic matter in wash-waters and effluent from the processing of fruits and vegetables determines whether they need to be treated to fulfil regulatory standards for their intended use. This research proposes a novel technique in photovoltaic cell-based renewable energy in saline water analysis using the oxidation process and deep learning techniques. Here, the saline water oxidation is carried out based on photovoltaic cell-based renewable and saline water analysis is carried out using Markov fuzzy-based Q-radial function neural networks (MFQRFNN). The plan is entirely web-oriented to enable better control and effective monitoring of water consumption. This monitoring makes use of a communication system that collects data in the form of irregularly spaced time series. Experimental analysis has been carried out based on water salinity data in terms of accuracy, precision, recall, specificity, computational cost, and kappa coefficient.
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DOI: 10.2166/wrd.2023.071
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