Simultaneous Energy and Water Optimisation in Shale Exploration

A Superstructure Based Optimization Approach for Regeneration Reuse of Water Network: Optimal Design of a Detailed Nanofiltration Regenerator Network

Increasing freshwater costs and environmental concerns have necessitated the adoption of strategies for reducing freshwater consumption and effluent water discharge in chemical processes. Regeneration technologies increase opportunities for water reuse and recycle, and nanofiltration has emerged as a competitive wastewater regeneration technology. However, the optimal design of nanofiltration networks has not been extensively investigated. This study presents a framework for the optimal design and synthesis of multicontaminant nanofiltration membrane regenerator networks for application in water minimization problems. Mathematical optimization technique is developed based on a superstructure containing all system components and streams, incorporating nanofiltration units, pumps, and energy recovery devices. A linear approach and the modified Spiegler-Kedem model are explored in modelling the nanofiltration, and the steric-hindrance pore model is used to characterize the membrane. The objective of the optimization is to simultaneously minimize the water consumption and the total annual cost of the network. Furthermore, the optimal size, configuration, membrane properties and operating conditions of the equipment are determined. The applicability of the model is illustrated using a case study of an integrated pulp and paper plant. It was found that detailed models with customized modules are more useful when compared to the linear “black box” approach and approaches using fixed module specifications. The customized, detailed design of the regenerator network increased freshwater savings by 24% when compared to a black-box model, 31% when compared to a detailed model with fixed module specifications and 41% when compared to a reuse-recycle system with no regeneration. Similarly, cost savings of 38, 35 and 36% respectively were obtained. A trade-off was noted between the energy costs and the other components of the objective function since more energy was required to facilitate the reduction of water consumption and capital requirements.

Optimal Design of a Hydrolysis Sugar Membrane Purification System Using a Superstructure-Based Approach

As an alternative to gasoline, bioethanol can be produced from lignocellulosic biomass through hydrolysis using an ionic solution containing zinc chloride (ZnCl2). This method allows for a high yield of glucose from lignocellulose, but entails the removal of ZnCl2 from the hydrolysate using multiple nanofiltration membranes before the fermentation of glucose. This paper presents a mathematical technique for designing such a multistage membrane separation system. The optimization model for the synthesis of membrane networks is based on a superstructure with all feasible interconnections between the membrane units, and consists of mass balances, logical constraints and product specifications. A case study of the separation of a bagasse hydrolysis solution is used to demonstrate the application of the proposed model. Results show that using both types of nanofiltration membranes allows higher ZnCl2 removal ratios at each membrane unit, hence a decrease in the number of membrane units required and a reduction of about 35% in capital cost compared to the cases in which only one membrane type is used. Further analysis is performed to examine the effect of membrane performance on the economics of the separation system.

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

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.

Special Issue on “Process Design, Integration, and Intensification”

With the growing emphasis on enhancing the sustainability and efficiency of industrial plants, process integration and intensification are gaining additional interest throughout the chemical engineering community [...]