Suchergebnisse

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

Multi-objective optimization (MOO) is widely used in engineering systems design and planning. The solution of a MOO problem leads to a set of efficient points (Pareto set) from which decision-makers should identify the one that best fits their preferences. Generating this set requires large computational efforts, and the post-optimal analysis of the solutions becomes difficult as the number of objectives increases. This work introduces an approach based on the Analytic Hierarchy Process (AHP) to overcome these limitations. Through the definition of an aggregated objective function calculated using the AHP algorithm, a single-objective model is constructed that provides a unique Pareto solution of the original MOO model. The AHP is combined with a mixed-integer non-linear programming (MINLP) formulation that simplifies its application and is particularly suited to deal with many objectives (like those arising in sustainable engineering problems). The capabilities of the approach are demonstrated through a case study addressing the sustainable sugar/ethanol supply chain design problem. ; The authors wish to acknowledge support from the CONICET, Argentina (project PIP 00785 and doctoral scholarship), and the Spanish Government (ENE2015-64117-C5-3-R, CTQ2016-77968-C3).

In this work, we introduce a non-convex MINLP optimization model for water management in shale gas production. The superstructure includes: reuse/recycle in the same or neighboring wellpad, treatment in mobile units or in centralized water treatment (CWT) facility, or transport to Class II disposal wells. We consider four different water qualities: flowback water, impaired water, desalinated water and freshwater. Additionally, water blending ratios are unrestricted and friction reducers expenses are calculated accounting for impaired water contamination. The objective is to optimize the fracturing schedule, the number of tanks needed in each time period, flowback destination (reuse, treated or disposal), and fracturing fluid composition by maximizing the "sustainability profit" (Zore et al., 2017). The problem is tackled in two steps. First, we solve an MILP model based on McCormick relaxations. Second, a smaller MINLP is solved in which some binary variables are fixed. The capabilities of the proposed mathematical model are validated against long-time horizon scenario from historical data of the Marcellus Shale play. ; 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 Economía, Industria y Competitividad» CTQ2016-77968-C3-02-P (FEDER, UE).

With the recent trend of moving towards a more sustainable economy, the interest on designing buildings with lower cost and environmental impact has grown significantly. In this context, multi-objective optimization has attracted much attention in building design as a tool to study trade-off solutions ("cost" vs "environmental impact") resulting from the optimization of conflicting objectives. One major limitation of this approach (as applied to building design) is that it is computationally demanding due to the need to optimize several objectives using complex models based on differential equations (which are used to estimate the energy consumed by a building). In this work, we propose a systematic framework for the design of buildings that combines a rigorous objective reduction method (which removes redundant objectives from the analysis) with a surrogate model (which simplifies the calculation of the energy requirements of the building), both of which expedite the identification of alternative designs leading to environmental improvements. The capabilities of our methodology are illustrated through a case study based on a thermal modelling of a house-like cubicle, in which we optimize the insulation thicknesses of the building envelope. Results show that significant economic and environmental improvements can be achieved compared to the base case (cubicle without insulation). Furthermore, it is clearly illustrated how the minimization of an aggregated environmental metric, like the Eco-Indicator 99, as unique environmental objective may overlook some Pareto solutions that may be appealing for decision-makers. ; The authors would like to acknowledge financial support from the Spanish Government (ENE2015-64117-C5-3-R (MINECO/FEDER, UE)). Joan Carreras would also like to acknowledge financial support from the Pump-Priming Research Programs of The University of Manchester.

Wastewater management is one of the main hurdles encountered by the shale gas industry for boosting overall process cost-effectiveness while reducing environmental impacts. In this light, this paper introduces a new multi-objective model for the thermo-economic and environmental optimization of solar-based zero-liquid discharge (ZLD) desalination systems. The solar-driven ZLD system is especially developed for desalinating high-salinity wastewaters from shale gas process. A decentralized system is proposed, encompassing a solar thermal system, a Rankine power cycle, and a multiple-effect evaporator combined with mechanical vapor recompression. The environment-friendly ZLD operation is ensured by specifying the salt concentration of brine discharges close to saturation conditions. The mathematical modelling approach is centered on a multi-objective non-linear programming (MoNLP) formulation, which is aimed at simultaneously minimizing thermo-economic and environmental objective functions. The latter objective function is quantified by the ReCiPe methodology based on life cycle assessment. The MoNLP model is implemented in GAMS software, and solved through the epsilon-constraint method. A set of trade-off Pareto-optimal solutions is presented to support decision-makers towards implementing more sustainable and cost-efficient solar-driven ZLD desalination systems. The comprehensive energy, economic and environmental analysis reveals that the innovative system significantly decreases costs and environmental impacts in shale gas wastewater operations. ; This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 640979.

Thermal membrane distillation (MD) is an emerging technology to desalinate high-salinity wastewaters, including shale gas produced water to reduce the corresponding water footprint of fracturing operations. In this work, we introduce a rigorous optimization model with energy recovery for the synthesis of multistage direct contact membrane distillation (DCMD) system. The mathematical model (implemented in GAMS software) is formulated via generalized disjunctive programming (GDP) and mixed-integer nonlinear programming (MINLP). To maximize the total amount of water recovered, the outflow brine is fixed close to salt saturation conditions (300 g·kg−1 water) approaching zero liquid discharge (ZLD). A sensitivity analysis is performed to evaluate the system's behavior under different uncertainty sources such as the heat source availability and inlet salinity conditions. The results emphasize the applicability of this promising technology, especially with low steam cost or waste heat, and reveal variable costs and system configurations depending on inlet conditions. For a produced water salinity ranging from 150 g·kg−1 water to 250 g·kg−1 water based on Marcellus play, an optimal treating cost are between 11.5 and 4.4 US$ m−3 is obtained when using low-cost steam. This cost can decrease to 2.8 US$ m−3 when waste heat from shale gas operations is used. ; This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 640979.

27th European Symposium on Computer Aided Process Engineering (ESCAPE 27), Barcelona, 1st-5th October, 2017. ; Environmental impacts related to increasing greenhouse gas emissions and depletion of fossil-fuel reserves and water resources are major global concerns. In this work, we introduce a new multi-objective optimization model for simultaneous synthesis of zero-emission desalination plants driven by renewable energy. The system is particularly developed for zero-liquid discharge (ZLD) desalination of high-salinity shale gas wastewater, aiming to enhance economic and environmental system performance. The mathematical model is based on a multistage superstructure, which integrates a solar assisted Rankine cycle to a multiple-effect evaporation with mechanical vapor recompression (MEE-MVR) plant. For achieving the goal of more sustainable ZLD process, we specify the discharge brine salinity near to salt saturation conditions. The model is formulated as a multi-objective multiperiod non-linear programming (NLP) problem. The model is implemented in GAMS and solved via epsilon-constraint method, through the minimization of total annualized cost and environmental impacts. The economic objective function accounts for capital cost of investment and operational expenses, while environmental criteria are quantified by the life cycle assessment (LCA)-based ReCiPe methodology. A case study is performed to demonstrate the capabilities of the developed model. The obtained set of trade-off Pareto-optimal solutions reveals that integration of renewable energy generation to ZLD desalination plants can lead to significant cost and environmental savings for shale gas industry. ; European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 640979.

27th European Symposium on Computer Aided Process Engineering (ESCAPE 27), Barcelona, 1st-5th October, 2017. ; Shale gas production requires significant water demand for well exploitation and a great volume of wastewater is generated since nearly 70 % of the drilling water returns to the surface [1], as flowback water (FBW) and produced water (PW), with different salinities. Their treatment has a double benefit: treated water can replace freshwater (FW) and besides waste volumes are reduced. Conventional desalination technologies can be appropriate for FBW but not for the hypersaline PW. Forward Osmosis (FO) is a promising alternative to deal with PW which can be used as a standalone desalination process or as an advanced pretreatment for other technologies [2]. In this work, we propose a superstructure that combines FO with Reverse Osmosis (RO). Its objective is twofold: to minimize FW consumption in well exploitation as well as the volume of final waste. The superstructure comprises a RO unit; two FO units; and mixers and splitters allowing connections between the units. In the figure, the FO1 and FO2 units act as pretreatments for the RO and as waste concentrators aiming for zero liquid discharge (ZLD). In the F01 unit, FBW is diluted and the sludge from the previous pretreatment (where other contaminants apart from Total Dissolved Solids (TDS) are removed) becomes concentrated. In the FO2 unit, the PW is diluted and the brine from the RO unit becomes concentrated. We formulated a bi-objective NonLinear Programming (NLP) problem that aims simultaneously to minimize the specific total cost ($/m3 drilling water) and the FW consumption (m3). The proposed approach is applied to a case study that uses 8500 m3/day of drilling water. The solution shows the trade-off between the cost and FW consumption and highlights the potential of FO to offer a solution for the treatment of the hypersaline PW and simultaneously reduce the shale gas waste volume. ; European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 640979.

27th European Symposium on Computer Aided Process Engineering (ESCAPE 27), Barcelona, 1st-5th October, 2017. ; In this work, we analyze the effect of shale gas well data uncertainty on the multiobjective optimization of a multistage direct contact membrane distillation (DCMD) model. The uncertain parameters, flowrate and salt concentration of the flowback water, are modelled by a set of correlated scenarios. A bi-criterion stochastic MINLP was formulated to minimize the expected total annual cost (TAC) and its variability, controlled by the worst case (WC) risk management metric. The model was solved using a modified version of the sample average approximation (SAA) algorithm, which decomposes the original problem into two: a deterministic MINLP model and a stochastic NLP model. The solution is a set of Pareto curves, where the two global extreme solutions provide the DCMD designs that achieve the minimum expected TAC and the minimum WC, respectively. Furthermore, both designs are able to satisfy the zero liquid discharge (ZLD) requirement imposed in the outflow stream. ; European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 640979.

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.

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.

Possible drawbacks of microreactors are inefficient reactant mixing and the clogging of microchannels when solid-forming reactions are carried out or solid (catalysts) suspensions are used. Ultrasonic irradiation has been successfully implemented for solving these problems in microreactor configurations ranging from capillaries immersed in ultrasonic baths to devices with miniaturized piezoelectric transducers. Moving forward in process intensification and sustainable development, the acoustic energy implementation requires a strategy to optimize the microreactor from an ultrasound viewpoint during its design. In this work, we present a simple analytical model that can be used as a guide to achieving a proper acoustic design of stacked microreactors. An example of this methodology was demonstrated through finite element analysis and it was compared with an experimental study found in the literature. ; This research is funded by the EU project MAPSYN: Microwave, Acoustic and Plasma SYNtheses, under grant agreement No. CP-IP 309376 of the European Union Seventh Framework Program.

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).

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. ; This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 640979. The financial support provided by the National Council for Scientific and Technological Development of Brazil (CNPq), under process No. 233953/2014-0 is also gratefully acknowledged.

Sustainable and efficient desalination is required to treat the large amounts of high-salinity flowback water from shale gas extraction. Nevertheless, uncertainty associated with well data (including water flowrates and salinities) strongly hampers the process design task. In this work, we introduce a new optimization model for the synthesis of zero-liquid discharge (ZLD) desalination systems under uncertainty. The desalination system is based on multiple-effect evaporation with mechanical vapor recompression (MEE-MVR). Our main objective is energy efficiency intensification through brine discharge reduction, while accounting for distinct water feeding scenarios. For this purpose, we consider the outflow brine salinity near to salt saturation condition as a design constraint to achieve ZLD operation. In this innovative approach, uncertain parameters are mathematically modelled as a set of correlated scenarios with known probability of occurrence. The scenarios set is described by a multivariate normal distribution generated via a sampling technique with symmetric correlation matrix. The stochastic multiscenario non-linear programming (NLP) model is implemented in GAMS, and optimized by the minimization of the expected total annualized cost. An illustrative case study is carried out to evaluate the capabilities of the proposed new approach. Cumulative probability curves are constructed to assess the financial risk related to uncertain space for different standard deviations of expected mean values. Sensitivity analysis is performed to appraise optimal system performance for distinct brine salinity conditions. This methodology represents a useful tool to support decision-makers towards the selection of more robust and reliable ZLD desalination systems for the treatment of shale gas flowback water. ; This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 640979.