Viviani C. Onishi

MINLP Model for the Synthesis of Heat Exchanger Networks with Handling Pressure of Process Streams

Abstract This paper introduces a new mathematical model for the simultaneous synthesis of heat exchanger networks (HENs), wherein the handling pressure of process streams is used to enhance the heat integration. The proposed approach combines generalized disjunctive programming (GDP) and mixed-integer nonlinear programming (MINLP) formulation, in order to minimize the total annualized cost composed by operational and capital expenses. A multi-stage superstructure is developed for the HEN synthesis, assuming constant heat capacity flow rates and isothermal mixing, and allowing for streams splits. In this model, the pressure and temperature of streams must be treated as optimization variables, increasing further the complexity and difficulty to solve the problem. In addition, the model allows for coupling of compressors and turbines to save energy. A case study is performed to verify the accuracy of the proposed model. In this example, the optimal integration between the heat and work decreases the need for thermal utilities in the HEN design. As a result, the total annualized cost is also reduced due to the decrease in the operational expenses related to the heating and cooling of the streams.

Could Sci-Hub become a quicksand for authors?

Sci-Hub has shaken the pillars of scholarly publishing, providing free access to millions of paywall-protected scientific articles. Along the way, it has also challenged the hegemony of major publishers and a system propelled by scientometrics. Here we posit a scenario in which the myriad of papers offered by Sci-Hub could trigger a sudden flip to gold open-access, dragging authors into an even more restricting paywall.

Multistage Membrane Distillation for the Treatment of Shale Gas Flowback Water: Multi-Objective Optimization under Uncertainty

Abstract In this work, we analyze the effect of shale gas well data uncertainty on the multi-objective 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.

MINLP Optimization Algorithm for the Synthesis of Heat and Work Exchange Networks

The financial supports by the Brazilian agency “Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior – CAPES” (process no 10758/12-7), and the Spanish Ministry of Science and Innovation and Ministry of Economy and Competitiveness (under project CTQ2012-37039-C02-02) are gratefully acknowledged.

Metal Bioaccumulation by Plants in Roadside Soils: Perspectives for Bioindication and Phytoremediation

Traffic-related metal pollution is a serious worldwide concern. Roadside soils are constantly subjected to the deposition of metals released by tailpipe gases, vehicle parts, and road infrastructure components. These metals, including platinum group elements from catalytic converters, constitute a threat to surrounding ecosystems that frequently comprise pasture and agricultural lands. Due to the capacity of plants to tolerate and accumulate metals, the study of the vegetation growing in soils adjacent to roads is important to understand their role as bioindicators of traffic-related metal pollution and infer their potential for the phytoremediation of roadside areas. This chapter reviews the main sources of metals in roadside soils and dusts, and the bioaccumulation of metals in plants growing alongside roads presenting different traffic loads and climatic conditions. The pertaining literature is discussed with a particular emphasis on the suitability of the assessed plant species to indicate and mitigate traffic-related metal pollution.

Desalination of shale gas wastewater: Thermal and membrane applications for zero-liquid discharge

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

Zero-Liquid Discharge Desalination of Hypersaline Shale Gas Wastewater: Challenges and Future Directions

Unconventional natural gas extraction from tight shale reservoirs, or “shale gas”, has recently emerged as an attractive energy resource to face the rising worldwide demand.

Combining Forward and Reverse Osmosis for Shale Gas Wastewater Treatment to Minimize Cost and Freshwater Consumption

Abstract One of the challenges for the future of the shale gas production industry is the water management due to the large demand of water for wells drilling and fracturing and the high volumes of liquid effluent produced. On-site treatment is a convenient option for the reuse of the shale wastewater as drilling water for subsequent wells, which simultaneously reduces the freshwater consumption and the waste volume. While conventional desalination technologies are suitable for the treatment of flowback water, they are not appropriate for the hypersaline produced water, which is typically disposed into underground injection wells. In this work, we propose a mathematical model to address the optimal design of an on-site treatment for both flowback and produced waters, combining reverse and forward osmosis, to simultaneously minimize the freshwater consumption and the specific cost of the fracturing water. The results obtained show a clear trade-off between both objectives and highlight the potential of the proposed technology combination to give an environmentally friendly solution to the shale gas produced water.

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.

Multi-Objective Optimization of Renewable Energy-Driven Desalination Systems

Abstract 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.