Performance prediction model for desalination plants using modified grey wolf optimizer based artificial neural network approach DOI Creative Commons
Yifan Yang, Chengpeng Wang, Shenghui Wang

и другие.

Desalination and Water Treatment, Год журнала: 2024, Номер 319, С. 100411 - 100411

Опубликована: Май 24, 2024

Desalination represents an effective method for alleviating water scarcity, applying algorithmic techniques to predict the performance of reverse osmosis (RO) desalination plants, Modified Grey Wolf Optimizer (MGWO) based Artificial Neural Networks (ANN) can membrane distillation (MD) equipment. Four experimental inputs are selected: feed salt concentration(35-140 g/h), flow rate(400-600 L/h), evaporator inlet temperature (60-80℃), and condenser (20-30℃). The permeate flux (L/h m2) is selected as output. Ten prediction models were proposed compared with existing (ANN, WOA-ANN, GWO-ANN). results showed that MGWO-ANN model-5 best regression results: R2=99.3%, mean square error (MSE)=0.004. This model outperformed ANN (R2=98.8%, MSE=0.060), WOA-ANN (R2=99.1%, MSE=0.005) GWO-ANN (R2=98.9%, MSE=0.007). Model-5 has a single hidden layer (H=1), 13 nodes (n=13), 10 search agents (SA=10), 75%-20%-05% dataset division. Its residual within acceptable limits (spanning -0.1 0.2). Optimizing number (n) (SA) improve training efficiency accuracy model, capable more accurately predicting plants.

Язык: Английский

Review of Battery-supercapacitor Hybrid Energy Storage Systems for Electric Vehicles DOI Creative Commons
Chandu V.V. Muralee Gopi,

R. Ramesh

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103598 - 103598

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

13
A comprehensive review of responsive draw solutes in forward osmosis: Categories, characteristics, mechanisms and modifications DOI

L. Zhang,

Xinyang Sun,

Simiao Wu

и другие.

Desalination, Год журнала: 2024, Номер 583, С. 117676 - 117676

Опубликована: Апрель 25, 2024

Язык: Английский

Процитировано

7
Coupling of photovoltaic thermal with hybrid forward osmosis-membrane distillation: Energy and water production dynamic analysis DOI
Ali Seid Ali, Tijani Bounahmidi

Journal of Water Process Engineering, Год журнала: 2024, Номер 64, С. 105710 - 105710

Опубликована: Июнь 29, 2024

Язык: Английский

Процитировано

5
A comprehensive review on forward osmosis mass transfer and fouling: Mathematical modeling, mechanism, prediction and optimization DOI

Y.K. Goi,

Mingzhou Li, Yong Yeow Liang

и другие.

Journal of Water Process Engineering, Год журнала: 2025, Номер 72, С. 107677 - 107677

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Carbon quantum dots for sustainable water treatment: A critical review on synthesis, properties, challenges, and applications in forward osmosis desalination technologies DOI

D. Dsilva Winfred Rufuss,

K. S. Sonu Ashritha

Chemical Engineering Journal, Год журнала: 2025, Номер unknown, С. 163059 - 163059

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Performance prediction model for desalination plants using modified grey wolf optimizer based artificial neural network approach DOI Creative Commons
Yifan Yang, Chengpeng Wang, Shenghui Wang

и другие.

Desalination and Water Treatment, Год журнала: 2024, Номер 319, С. 100411 - 100411

Опубликована: Май 24, 2024

Desalination represents an effective method for alleviating water scarcity, applying algorithmic techniques to predict the performance of reverse osmosis (RO) desalination plants, Modified Grey Wolf Optimizer (MGWO) based Artificial Neural Networks (ANN) can membrane distillation (MD) equipment. Four experimental inputs are selected: feed salt concentration(35-140 g/h), flow rate(400-600 L/h), evaporator inlet temperature (60-80℃), and condenser (20-30℃). The permeate flux (L/h m2) is selected as output. Ten prediction models were proposed compared with existing (ANN, WOA-ANN, GWO-ANN). results showed that MGWO-ANN model-5 best regression results: R2=99.3%, mean square error (MSE)=0.004. This model outperformed ANN (R2=98.8%, MSE=0.060), WOA-ANN (R2=99.1%, MSE=0.005) GWO-ANN (R2=98.9%, MSE=0.007). Model-5 has a single hidden layer (H=1), 13 nodes (n=13), 10 search agents (SA=10), 75%-20%-05% dataset division. Its residual within acceptable limits (spanning -0.1 0.2). Optimizing number (n) (SA) improve training efficiency accuracy model, capable more accurately predicting plants.

Язык: Английский

Процитировано

2