A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty

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.

Process Synthesis, Analysis, and Optimization Methodologies toward Chemical Process Sustainability

“…Robustness index maximization contrasted with uncertainties that allowed categorizing optimal process interconnections. Process synthesis under uncertainties approach was implemented to generate alternatives for planning wastewater treatment systems of shale gas/oil with multiples energy sources . A new outline was resulted, counting fossil and solar energy under uncertainties, considering wastewater treatment and water-management systems in shale gas production.…”

Greenhouse gases emissions in liquified natural gas as a marine fuel: Life cycle analysis and reduction potential

“…Membrane technologies have been shown to be effective in removing major pollutants from shale gas wastewater and rendering the treated water usable for hydraulic fracturing and other applications. [ 37,38 ] When reverse osmosis is selected to treat the shale gas wastewater, the energy consumption ranges between 1–9 kWh e /m 3 . [ 39 ] If an energy consumption of 9 kWh e /m 3 is used and the emission factor of 600 g CO 2eq /kWh e for natural gas as the source of energy is selected (Table 3), the carbon footprint is 2.34*10 −3 g CO 2eq /MJ, which is quite negligible compared to the WtP footprint of shale gas.…”

Models and hierarchical methodologies for evaluating solar energy availability under different sky conditions toward enhancing concentrating solar collectors use: Texas as a case study

“…3 [87], which represents one of the significant Fig. 1 A hierarchical methodology of predicting DNI hotspots in the United States due to various activities of water-energy-food nexus [88] such as shale oil and gas production [89][90][91], agricultural production [92,93], etc. The solar data for San Antonio is obtained from the National Solar Radiation Data Base (NSRDB) between 1991 and 2010 are: hourly global solar irradiance, hourly direct solar irradiance, hourly diffuse solar irradiance, hourly solar incidence angle, hourly dry bulk temperature, hourly wet bulk temperature, and relative humidity.…”