Sustainable supply chain design under correlated uncertainty in energy and carbon prices

This paper aims to provide an improvement in the modeling of supply chain designs by incorporating correlated uncertainty among multiple parameters, resulting in a more resilient design. A new methodology to generate forecasts for historically correlated time series, regardless of their underlying probability distributions, is presented and applied to generate scenarios for energy and carbon prices, which historically proved to be correlated. These scenarios are then used in a stochastic computation to obtain a three-echelon supply chain design in Europe maximizing the economic performance. The emissions were monetarized through the incorporation of the European Union cap-and-trade emissions trading system into the model. The social impact of the supply chain network is measured in terms of the direct, indirect and induced jobs it creates, which are proportional to the economic performance. By combining the developed methodology with data mining algorithms, a reduction in the number of required scenarios by more than 90% was achieved. The numerical case study moreover shows that the stochastic design ensures an average reduction of emissions by more than 3 ktons compared to the use of a deterministic approach. In comparison, the computation of a stochastic supply chain design without parameter correlation takes 5 times longer.The authors gratefully acknowledge financial support to the Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital of the Generalitat Valenciana, Spain, under project PROMETEO/2020/064 and project PID2021-124139NB-C21

Design of a Cooperative Sustainable Three-Echelon Supply Chain under Uncertainty in CO2 Allowance

Driven by the growing concern regarding greenhouse gas emissions, in this work, we provide a robust stochastic model for the design of a cooperative supply chain (SC) under uncertainty in CO2 allowance prices from the European Union Emissions Trading System (EU ETS). During the last years, CO2 allowance prices have undergone unexpected changes, having strong impact on the design and management of optimal SC. The consideration of uncertainty in the allowance prices has therefore become more important. We use an autoregressive integrated moving average (ARIMA) model to predict future allowance prices. A full discretization of the underlying probability space leads to a number of scenarios far too large to be handled, so we compare two approaches to reduce the number of scenarios to a feasible maximum, the ScenRed algorithm and K-means clustering. The obtained results are compared with a deterministic approach that is widely studied in the literature, showing an increase in the benefits and a reduction of emissions.The authors gratefully acknowledge financial support to the Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital of the Generalitat Valenciana, Spain, under project PROMETEO/2020/064

Water Management in Shale Gas: A Perspective from the Cooperative Games Theory

This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 640979

Shale Water Desalination: Multistage membrane distillation considering different configurations and heat integration

This work introduces a simultaneous synthesis of membrane distillation systems with heat exchanger networks (HENs) for desalinating shale gas flowback and produce water. The direct contact and vacuum membrane configurations are the best options for desalination. Moreover, multistage membrane distillation systems usually have higher efficiencies than single-stages processes. For this reason, two different mathematical models for synthetizing multistage direct contact membrane distillation (MSDCMD) and multistage vacuum membrane distillation (MSVMD) are developed and optimized to achieve zero liquid discharge (ZLD) conditions. To this aim, brine discharges are considered to be near to the salt saturation conditions. The multi-stage superstructures are implemented in GAMS and optimized by SBB solver. The mathematical model is formulated via generalized disjunctive programming (GDP) and mixed-integer nonlinear programming (MINLP), to minimize the total annualized cost.This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 640979

Systematic Tools for the Conceptual Design of Inherently Safer Chemical Processes

Society is continuously facing challenges for safer chemical plants design, which is usually driven by economic criteria during the early steps of the design process, relegating safety concerns to the latest stages. This paper highlights the synergy of merging Process System Engineering tools with inherent safety principles. First, we design a superstructure that comprises several alternatives for streams, equipment, and process conditions, which exhibit different performance of economic and inherently safer indicators, the total annualized cost, and the Dow’s Fire and Explosion Index, respectively. The solution to this multiobjective problem is given by a Pareto set of solutions that indicates the existing trade-off between both objectives. The capabilities of the proposed framework are illustrated through two case studies, which solutions provide valuable insights into the design problem and are intended to guide decision-makers toward the adoption of inherently safer process alternatives.The authors acknowledge financial support from “Proyectos de l+D para grupos de investigación emergentes GV/2016/005” (Conselleria d’Educació, Investigació, Cultura i Esport, GENERALITAT VALENCIANA) and from the Spanish “Ministerio de Economía, Industria y Competitividad” (CTQ2016-77968-C3-02-P, AEI/FEDER, UE)

Economic and environmental strategic water management in the shale gas industry: Application of cooperative game theory

In this work, a mixed‐integer linear programming (MILP) model is developed to address optimal shale gas‐water management strategies among shale gas companies that operate relatively close. The objective is to compute a distribution of water‐related costs and profit among shale companies to achieve a stable agreement on cooperation among them that allows increasing total benefits and reducing total costs and environmental impacts. We apply different solution methods based on cooperative game theory: The Core, the Dual Core, the Shapley value, and the minmax Core. We solved different case studies including a large problem involving four companies and 207 wells. In this example, individual cost distribution (storage cost, freshwater withdrawal cost, transportation cost, and treatment cost) assigned to each player is included. The results show that companies that adopt cooperation strategies improve their profits and enhance the sustainability of their operations through the increase in recycled water.The authors gratefully acknowledge the financial support by the Ministry of Economy, Industry, and Competitiveness from Spain, under the projects CTQ2016-77968-C3-1-P and CTQ2016-77968-C3-2-P (AEI/FEDER, UE)

Optimal Shale Gas Flowback Water Desalination under Correlated Data Uncertainty

Presentation at the 27th European Symposium on Computer-Aided Process Engineering (ESCAPE-27), Barcelona, 2017, 1-5 October.Optimal flowback water desalination is critical to improve overall efficiency and sustainability of shale gas production. Nonetheless, great uncertainty in well data from shale plays strongly hinders the design task. In this work, we introduce a new stochastic multiscenario optimization model for the robust design of desalination systems under uncertainty. A zero-liquid discharge (ZLD) system composed by multiple-effect evaporation with mechanical vapor recompression (MEE-MVR) is proposed for the desalination of high-salinity shale gas flowback water. Salinity and flowrate of flowback water are both considered as uncertain design parameters, which are described by correlated scenarios with given probability of occurrence. The set of scenarios is generated via Monte Carlo sampling technique from a multivariate normal distribution. ZLD operation is ensured by the design constraint that allows brine concentration near to salt saturation conditions for all scenarios. The stochastic multiscenario nonlinear programming (NLP) model is optimized in GAMS, through the minimization of the expected total annualized cost. Risk analysis based on cumulative probability curves is performed in the uncertain search space, to support decision-makers towards the selection of more robust ZLD desalination systems applied to shale gas flowback water.This project has received funding from the European Union's Horizon 2020 research and innovation program under grand agreement No 640979

Shale gas flowback water desalination: Single vs multiple-effect evaporation with vapor recompression cycle and thermal integration

This paper introduces a new optimization model for the single and multiple-effect evaporation (SEE/MEE) systems design, including vapor recompression cycle and thermal integration. The SEE/MEE model is specially developed for shale gas flowback water desalination. A superstructure is proposed to solve the problem, comprising several evaporation effects coupled with intermediate flashing tanks that are used to enhance thermal integration by recovering condensate vapor. Multistage equipment with intercooling is used to compress the vapor formed by flashing and evaporation. The compression cycle is driven by electricity to operate on the vapor originating from the SEE/MEE system, providing all the energy needed in the process. The mathematical model is formulated as a nonlinear programming (NLP) problem optimized under GAMS software by minimizing the total annualized cost. The SEE/MEE system application for zero liquid discharge (ZLD) is investigated by allowing brine salinity discharge near to salt saturation conditions. Additionally, sensitivity analysis is carried out to evaluate the optimal process configuration and performance under distinct feed water salinity conditions. The results highlight the potential of the proposed model to cost-effectively optimize SEE/MEE systems by producing fresh water and reducing brine discharges and associated environmental impacts

Holistic Planning Model for Sustainable Water Management in the Shale Gas Industry

To address water planning decisions in shale gas operations, we present a novel water management optimization model that explicitly takes into account the effect of high concentrations of total dissolved solids (TDS) and temporal variations in the impaired water. The model comprises different water management strategies: (a) direct wastewater reuse, which is possible because of new additives tolerant to high TDS concentrations but at the expense of increasing the costs; (b) wastewater treatment, separately taking into account pretreatment, softening, and desalination technologies; and (c) the use of Class II disposal sites. The objective is to maximize the “sustainability profit” by determining the flowback destination (reuse, degree of treatment, or disposal), the fracturing schedule, the fracturing-fluid composition, and the number of water-storage tanks needed for each period of time. Because of the rigorous determination of TDS in all water streams, the model is a nonconvex MINLP model that is tackled in two steps: first, an MILP model is solved on the basis of McCormick relaxations; next, the binary variables that determine the fracturing schedule are fixed, and a smaller MINLP is solved. Finally, several case studies based on Marcellus Shale Play are optimized to illustrate the effectiveness of the proposed formulation. The model identifies direct reuse as the best water-management option to improve both economic and environmental criteria.This project has received funding from the European Union’s Horizon 2020 Research and Innovation Program under grant agreement no. 640979 and from the Spanish Ministerio de Economiá , Industria y Competitividad CTQ2016-77968-C3-02-P (FEDER, UE)