A general optimal operating strategy for commercial membrane distillation facilities. (August 2020)
- Record Type:
- Journal Article
- Title:
- A general optimal operating strategy for commercial membrane distillation facilities. (August 2020)
- Main Title:
- A general optimal operating strategy for commercial membrane distillation facilities
- Authors:
- Gil, Juan D.
Mendes, Paulo R.C.
Camponogara, E.
Roca, Lidia
Álvarez, J.D.
Normey-Rico, Julio E. - Abstract:
- Abstract: The high thermal energy consumption is one of the main drawbacks hampering the commercial implementation of Membrane Distillation (MD) technology. The development of adequate operating strategies can help to reduce these energy requirements. Accordingly, this paper focuses on the optimal management of the array of MD modules composing a commercial-scale MD plant, trying to reduce their thermal energy consumption while ensuring a given water need. For this aim, the array of MD modules is modelled as a Mixed Integer Programming (MIP) system to consider that some modules can be turned on/off depending on the operation specifications. An algorithm based on the Generalized Bender Decomposition (GBD) is then developed for the efficient solution of the problem. This algorithm is incorporated in a Model Predictive Control (MPC) strategy allowing to manage the plant in real time. The effectiveness of the proposed strategy is verified using a practical example. The obtained results are compared with a manual and a previous strategy presented in literature, showing that for a sunny day, around the 65 and 55% of the thermal energy consumed by these methodologies can be saved, which means important thermal energy savings that can be relevant for the industrial implementation of MD technology. Highlights: An optimal management of an industrial-scale membrane distillation plant is addressed. A controller based on the Generalized Benders Decomposition method is proposed. TheAbstract: The high thermal energy consumption is one of the main drawbacks hampering the commercial implementation of Membrane Distillation (MD) technology. The development of adequate operating strategies can help to reduce these energy requirements. Accordingly, this paper focuses on the optimal management of the array of MD modules composing a commercial-scale MD plant, trying to reduce their thermal energy consumption while ensuring a given water need. For this aim, the array of MD modules is modelled as a Mixed Integer Programming (MIP) system to consider that some modules can be turned on/off depending on the operation specifications. An algorithm based on the Generalized Bender Decomposition (GBD) is then developed for the efficient solution of the problem. This algorithm is incorporated in a Model Predictive Control (MPC) strategy allowing to manage the plant in real time. The effectiveness of the proposed strategy is verified using a practical example. The obtained results are compared with a manual and a previous strategy presented in literature, showing that for a sunny day, around the 65 and 55% of the thermal energy consumed by these methodologies can be saved, which means important thermal energy savings that can be relevant for the industrial implementation of MD technology. Highlights: An optimal management of an industrial-scale membrane distillation plant is addressed. A controller based on the Generalized Benders Decomposition method is proposed. The control method is aimed at reducing the thermal energy consumption. A case study based on two real plants located in Almer__a is used as testbed. The results demonstrate the bene_ts achievable through the developed approach. … (more)
- Is Part Of:
- Renewable energy. Volume 156(2020)
- Journal:
- Renewable energy
- Issue:
- Volume 156(2020)
- Issue Display:
- Volume 156, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 156
- Issue:
- 2020
- Issue Sort Value:
- 2020-0156-2020-0000
- Page Start:
- 220
- Page End:
- 234
- Publication Date:
- 2020-08
- Subjects:
- Thermal efficiency -- Desalination -- Solar energy -- Benders decomposition -- Model predictive control
Renewable energy sources -- Periodicals
Power resources -- Periodicals
Énergies renouvelables -- Périodiques
Ressources énergétiques -- Périodiques
333.794 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09601481 ↗
http://www.elsevier.com/journals ↗
http://www.journals.elsevier.com/renewable-energy/ ↗ - DOI:
- 10.1016/j.renene.2020.04.074 ↗
- Languages:
- English
- ISSNs:
- 0960-1481
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7364.187000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13469.xml