| Title |
Innovative artificial neural network approach for integrated biogas - wastewater treatment system modelling: Effect of plant operating parameters on process intensification |
| ID_Doc |
10100 |
| Authors |
Sakiewicz, P; Piotrowski, K; Ober, J; Karwot, J |
| Title |
Innovative artificial neural network approach for integrated biogas - wastewater treatment system modelling: Effect of plant operating parameters on process intensification |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.rser.2020.109784 |
| Abstract |
An anaerobic fermentation process for biogas production integrated with wastewater purification in a modern wastewater treatment plant (WWTP) of designed nominal capacity 27,000 m(3)/day was modelled using artificial neural networks (ANNs). Neural models were trained, validated, and tested based on real-scale industrial data (covering three years of continuous plant operation), considering both technological aspects of the process and treated wastewater quality. An innovative approach addressing the simultaneous effect of seven adjustable main plant operation parameters together with wastewater characteristics (five parameters) on biogas production is reported for the first time in the literature. A parameter sensitivity analysis indicated clearly the higher importance of the operation process parameters on the biogas yield compared to the wastewater quality (COD, BOD5, TSS, P-g, N-g). The operation process parameters were the subject of modelling and analysis in respect to new, innovative possibilities, and technological strategies for biogas yield enhancement. The ANN model presented can be used as a predictive tool, an important element in such complex processes as steering/control strategies or for their optimisation procedures, as well as in the testing of other promising process intensification and optimisation scenarios. |
| Author Keywords |
Biogas production; Wastewater treatment plant; Artificial neural network; Numerical model; Plant-operating parameters; Sensitivity study |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000521976000014 |
| WoS Category |
Green & Sustainable Science & Technology; Energy & Fuels |
| Research Area |
Science & Technology - Other Topics; Energy & Fuels |
| PDF |
https://doi.org/10.1016/j.rser.2020.109784
|