article · Alexandria Engineering Journal
Membrane desalination combines thermal and separation distillation processes to purify saltwater. To design these systems effectively, engineers need reliable methods to forecast freshwater production and thermodynamic behaviour without conducting expensive physical trials. A machine-learning approach was developed to forecast the permeate flux of a tubular direct contact membrane distillation system. The model pairs a long short-term memory neural network with an election-based optimisation algorithm. It relies on four operational inputs: feed flow temperature, permeate temperature, feed flow rate, and feed salinity. When evaluated against models enhanced by alternative techniques, including grey wolf and sine-cosine optimisation algorithms, this model achieved superior predictive accuracy. It achieved a testing coefficient of determination of 0.988 and a root mean square error of 4.180, providing a practical methodology for sizing system parameters.
Designing efficient membrane desalination plants usually requires extensive and costly physical experimentation. Using reliable machine-learning models to predict freshwater output from standard operating conditions, such as temperature, flow rate, and salinity, streamlines the engineering workflow. This helps reduce early development expenditure while accelerating the deployment of efficient water purification infrastructure in areas facing water scarcity.
This model could be utilised by desalination system designers and engineering consultancies to optimise plant sizing parameters and forecast freshwater production rates. The work remains at an early computational stage, having been trained and tested on algorithmic performance metrics. Moving towards commercial engineering software tools would require further validation within real-world pilot or commercial desalination installations.
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Membrane desalination (MD) is an efficient process for desalinating saltwater, combining the uniqueness of both thermal and separation distillation configurations. In this context, the optimization strategies and sizing methodologies are developed from the balance of the system’s energy demand. Therefore, robust prediction modeling of the thermodynamic behavior and freshwater production is crucial for the optimal design of MD systems. This study presents a new advanced machine-learning model to obtain the permeate flux of a tubular direct contact membrane distillation unit. The model was established by optimizing a long-short-term memory (LSTM) model by an election-based optimization algorithm (EBOA). The model inputs were the temperatures of permeate and the feed flow, and the rate and salinity of the feed flow. The optimized model was compared with other optimized LSTM models by sine–cosine optimization algorithm (SCA), artificial ecosystem optimizer (AEO), and grey wolf optimization algorithm (GWO). All models were trained, tested, and evaluated using different accuracy measures. LSTM-EBOA outperformed other models in predicting the permeate flux based on different accuracy measures. LSTM-EBOA had the highest coefficient of determination of 0.998 and 0.988 and the lowest root mean square error of 1.272 and 4.180 for training and test, respectively. It can be recommended that this paper provide a useful pathway for sizing parameters selection and predicting the performance of MD systems that makes an optimally designed model for predicting the freshwater production rates without costly experiments.
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DOI: 10.1016/j.aej.2023.12.012
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