Back to Search
Start Over
Application of novel hybrid deep leaning model for cleaner production in a paper industrial wastewater treatment system
- Source :
- Journal of Cleaner Production. 294:126343
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Developing monitoring system for paper industrial wastewater treatment system is an important route for wastewater reuse and recycling from wastewater, which are regarded as effective way for cleaner production. A novel hybrid deep leaning CLSTMA model, which based on sequential fusion convolutional neural network (CNN), long short term memory (LSTM) and attention mechanism (AM), was developed to monitor the water quality in a full-scale paper industrial wastewater treatment system for energy conservation and emissions reduction. The hybrid CLSTMA model for predicting water quality of paper industrial wastewater treatment system was divided into three steps: spatial information fusion by using CNN module, temporal information fusion by using LSTM module and variable weighted calculation by using AM module. Compare with other models (CNN, LSTM and CLSTM models), RMSE of CLSTMA model for the effluent chemical oxygen demand (CODeff) reduced by 23.3–31.55%, MAE of CLSTMA model reduced by 38.89–74.50%, R of CLSTMA model increased by 8.29–11.86%. For the effluent suspended solids (SSeff), compared with CNN and LSTM models, RMSE of CLSTMA model reduced by 10.26% and 9.92%, MAE of CLSTMA model reduced by 5.37% and 3.44%, R of CLSTMA model increased by 15.13% and 37.21%, respectively. While, R of CLSTMA was consistent with CLSTM model, but RMSE and MAE of CLSTMA model reduced by 16.07% and 7.49% than the CLSTM model. Simulation results demonstrate that the proposed CLSTMA model has a great potential in monitoring paper industrial wastewater treatment system for cleaner production.
- Subjects :
- Suspended solids
Renewable Energy, Sustainability and the Environment
business.industry
020209 energy
Strategy and Management
05 social sciences
Chemical oxygen demand
02 engineering and technology
Building and Construction
Convolutional neural network
Industrial and Manufacturing Engineering
Energy conservation
Industrial wastewater treatment
Wastewater
050501 criminology
0202 electrical engineering, electronic engineering, information engineering
Environmental science
Cleaner production
Process engineering
business
Effluent
0505 law
General Environmental Science
Subjects
Details
- ISSN :
- 09596526
- Volume :
- 294
- Database :
- OpenAIRE
- Journal :
- Journal of Cleaner Production
- Accession number :
- edsair.doi...........9495e849097d4ef14e39e63c1e2ae85b