Abstract
The production of shale gas and oil is associated with the generation of substantial amounts of wastewater. With the growing emphasis on sustainable development, the energy sector has been intensifying efforts to manage water resources while diversifying the energy portfolio used in treating wastewater to include fossil and renewable energy. The nexus of water and energy introduces complexity in the optimization of the water management systems. Furthermore, the uncertainty in the data for energy (e.g., solar intensity) and cost (e.g., price fluctuation) introduce additional complexities. The objective of this work is to develop a novel framework for the optimizing wastewater treatment and water-management systems in shale gas production while incorporating fossil and solar energy and accounting for uncertainties. Solar energy is utilized via collection, recovery, storage, and dispatch of heat. Heat integration with an adjacent industrial facility is considered. Additionally, electric power production is intended to supply a reverse osmosis (RO) plant and the local electric grid. The optimization problem is formulated as a multi-scenario mixed integer non-linear programming (MINLP) problem that is a deterministic equivalent of a two-stage stochastic programming model for handling uncertainty in operational conditions through a finite set of scenarios. The results show the capability of the system to address water-energy nexus problems in shale gas production based on the system’s economic and environmental merits. A case study for Eagle Ford Basin in Texas is solved by enabling effective water treatment and energy management strategies to attain the maximum annual profit of the entire system while achieving minimum environmental impact.
Highlights
The recent advancement in hydraulic fracturing technology and horizontal drilling has contributed to considerable growth in shale gas production
Once the foregoing steps are achieved and the total thermal and electric loads are determined of the integrated system, the optimization problem is formulated as a multi-scenario mixed integer non-linear programming (MINLP) problem that is a deterministic equivalent of a two-stage stochastic programming model with recourse to dealing with an uncertainty of solar energy and fossil fuels price for each period, more detailed information in Sections 3.2 and 3.4
This process consists of a boiler, a steam turbine, and a condenser, which is replaced with a multiple-effect distillation plant to exploit the surplus heat production
Summary
The recent advancement in hydraulic fracturing technology and horizontal drilling has contributed to considerable growth in shale gas production. Mirkhani and Saboohi [32] enhanced the limited capability of a deterministic energy supply model to handle the uncertainty in the price of natural gas and to incorporate renewable energy technologies in an effective method. The system consists of several subsystems which include: Cogeneration process (including non-condensing (back-pressure) steam turbine and water-tube boiler fueled with gas or oil), steam generator, solar collection process (parabolic trough collectors), thermal energy storage, multi-effect distillation plant, reverse osmosis plant, primary and secondary water treatment processes, and an industrial process. Two uncertain operational parameters (normal direct irradiance, and fossil fuel price) are considered in the model through a scenario-based approach, which represents a finite set of scenarios (or realizations) to describe the uncertain parameters and future outcomes with a certain probability for each of them. Heat integration is carried out among the hot and cold streams of an industrial process and subsystems of the entire system
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