Performance prediction of solar still with a high-frequency ultrasound waves atomizer using random vector functional link/heap-based optimizer. (August 2022)

• Record Type: Journal Article
• Authors: Elaziz, Mohamed Abd; El-Said, Emad M.S.; Elsheikh, Ammar H.; Abdelaziz, Gamal B.
• Is Part Of: Advances in engineering software. Volume 170(2022)
• Publication Date: 2022-08
• Subjects: Solar still -- Enhancement -- Ultrasonic wave atomizer -- HDH -- Artificial neural network, machine learning -- RVFL; ADW Agricultural drainage water -- ANN Artificial Neural Network -- CFD Computational Fluid Dynamics -- CRH Corporate rank hierarchy -- HBO Heap-based optimizer -- HFUWA High-frequency ultrasound waves atomizer -- ICA Imperialist Competition Algorithm -- ITE Instantaneous thermal efficiency -- LSTM Long short-term memory -- ML Machine-learning -- MRFO Manta ray foraging optimizer -- MSE Mean squared error -- ORR Ratio of operation recovery -- RVFL Random vector functional link -- SCA Sine cosine algorithm -- SE Solar Energy -- SS Solar Still -- ήth Thermal efficiency; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103142
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Performance of solar still with ultrasound atomizer was predicted. Using measurements, algorithms RVFL, R-SCA, R-MRFO & R-HBO were trained and tested. R-HBO has a high ability to find out the process responses as a function of inputs. The RVFL-HBO model has the best predicted values due to its excellent accuracy. RVFL-HBO achieves low RMSE, MRE, MAE and COV of yield and water temperature. Abstract: In this work, the thermal performance of a single slop solar still (SS) using a high-frequency ultrasound waves atomizer (HFUWA) as a humidifier was predicted through developing an artificial intelligence model. To minimize or avoid mathematical analysis or performing costly experimental work, a set of machine-learning (ML) algorithms were performed to predict the SS productivity and basin water temperature. A powerful ML algorithm called random vector functional link network (RVFL) is optimized using three different advanced metaheuristic optimizers, namely sine cosine algorithm (SCA), manta ray foraging optimizer (MRFO), heap-based optimizer (HBO). These optimizers are used to obtain the optimal parameters of RVFL model that maximize its prediction accuracy. By using experimental data, the algorithms; RVFL, R-SCA, R-MRFO and R-HBO, were trained and tested. The operational variables such as number of atomizers, water depth, and on-off time were used as input variables of the algorithms. The appropriate meteorological variables: atmospheric temperature, speed of wind, and solar intensity were considered as the input parameters. Results indicated that the R-HBO has a high ability to find out the process responses as a function of inputs in nonlinear relationship. RVFL-HBO achieves low RMSE, MRE, MAE and COV of 44.840, 0.512, 35.497 and 48.539 for water yield and 6.660, -0.019, 5.090 and 13.856 for water temperature. The low values of RMSE, MAE, MRE, and COV obtained by RVFL-HBO indicate its high accuracy over other models. Hence, it can be considered as the best choice for modeling the water desalination process in SS.