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Natural gas hydrate is considered as a new sustainable energy resource with huge potential and the increasing demand for green fuel hydrogen provides an opportunity for adopting alternative routines to produce hydrogen. The novel integrated system: natural gas hydrate exploitation by CO2/H2 replacement integrated with in situ methane reforming, can simultaneously produce H2 and sequestrate CO2. However, there is still a lack of in-depth research on the energy efficiency and economic performance of the novel integrated system. Therefore, we conducted a process design, modeling and simulation, exergy analysis, process integration, and sensitivity analysis of the integrated system for the first time. When the CO2 composition of the injected gas is 72 mol %, that is, the CH4 composition of the produced gas is 48.07 mol %, the exergy ratio of the integrated system after process integration is 1.54. The operating profit is 6.41 MM$/y. The exergy efficiency of the methane reforming subsystem is 0.62. The CO2 emission from methane reforming subsystem can be sequestrated through the natural gas hydrate exploitation by CO2/H2 replacement, achieving CO2 sequestration amount of 3914 kg/h with the CO2 sequestration ratio of 65%. The CO2 sequestration amount, CH4 composition in the produced gas, exergy ratio, and operating profit of the integrated system decrease as the CO2 composition of the injected gas decreases. Finally, sensitivity analysis results reveal that increasing the CH4 composition of the produced gas, minimizing water production, and increasing gas production in the exploitation unit will improve the energy performance of the novel integrated system. This work can guide the evaluation of energy efficiency and economy for the subsequent commercial exploitation of natural gas hydrate.

This study develops a framework for water-energy nexus optimization in shale gas production and distribution network under uncertainty. Sustainable design of the network is achieved through treatment of wastewater using thermal membrane distillation, whereby a comprehensive design model is integrated within the network to account for energy requirement of the unit. The proposed model also accounts for the problem of scheduling hydraulic fracturing using a continuous time formulation. Various uncertainties are associated with the network. Among different uncertain variables, uncertainties associated with price and demand are crucial as they can affect the optimal configuration significantly. Incorporation of uncertainty in the model seeks to meet the demand of natural gas consumers whilst accounting for the uncertainties associated with the price of final products. Uncertainties are modelled via randomly generated scenarios using beta distribution of the price generated using historical data. The stochastic model is applied to a case study through the maximization of net profit. Three different scenarios are considered for analysis. Results from the three different scenarios show 14.39%, 11.49%, and 12.34% increase in profit respectively as compared to the deterministic approach. Solving all the scenarios together gives an ensemble-average solution with 13.74% increase in expected profit. Savings in the freshwater requirement for fracturing and the energy associated with water management amounted to 23.2% and 42.7%, respectively.

Recent discoveries of substantial reserves of natural/shale gas have spurred growth in the production of methanol from natural gas. Such growth must entail the development of reliable infrastructures and supply chains. This paper presents a mathematical programming model for the strategic planning of the methanol supply chains to satisfy regional demands. To encourage utilization of local resources, economic incentives are considered. The uncertainties associated with the unit sale cost, supply of natural gas, and demand of methanol are incorporated. Several sustainability objectives are included such as minimizing fresh water consumption, minimizing CO2 emissions, and maximizing profit. To illustrate the applicability of the devised methodology, a case study from Mexico was solved with two schemes: Scheme A does not consider the uncertainty or economic incentives and Scheme B considers uncertainty, economic incentives and restrictions on the importation of methanol. The results show that by encouraging the national production of new methanol plants, the incentives generated are up to 113% better than if the importation and production of the only existing plant in Mexico are used.

The objective of this work is to develop a systematic approach for the design and scheduling of a novel skid-mounted solar-assisted membrane distillation (MD) system that can be used for domestic purposes and in remote areas that are not supported by fresh water infrastructure. Storage tanks are used to collect thermal energy and to supply the feed to the MD system while PV cells are connected to electric-energy storage batteries to drive the pumping. Gas tanks are used to supplement the solar thermal energy as needed. An optimization formulation is developed to determine the number and size of the storage tanks, the operating variables, the collection and dispatch times, the extent of solar and fossil-energy uses, and the operating schedule for the integrated system. A case study is solved to illustrate the merits of the proposed desalination system and design and scheduling approach.

This paper reviews principal concepts, tools, and metrics for risk management and Inherently Safer Design (ISD) during the conceptual stage of process design. Even though there has been a profusion of papers regarding ISD, the targeted audience has typically been safety engineers, not process engineers. Thus, the goal of this paper is to enable process engineers to use all the available design degrees of freedom to mitigate risk early enough in the design process. Mainly, this paper analyzes ISD and inherent safety assessment tools (ISATs) from the perspective of inclusion in conceptual process design. The paper also highlights the need to consider safety as a major component of process sustainability. In this paper, 73 ISATs were selected, and these tools were categorized into three groups: hazard-based inherent safety assessment tools (H-ISATs) for 22 tools, risk-based inherent safety assessment tools (R-ISATs) for 33 tools, and cost-optimal inherent safety assessment tools (CO-ISATs) for 18 tools. This paper also introduces an integrated framework for coordinating the conventional process design workflow with safety analysis at various levels of detail.

This work presents a mathematical model for the simultaneous optimisation of water and energy usage in hydraulic fracturing using a continuous time scheduling formulation. The recycling/reuse of fracturing water is achieved through the purification of flowback wastewater using thermally driven membrane distillation (MD). A detailed design model for this technology is incorporated within the water network superstructure in order to allow for the simultaneous optimisation of water, operation, capital cost, and energy used. The study also examines the feasibility of utilising the co-produced gas that is traditionally flared as a potential source of energy for MD. The application of the model results in a 22.42% reduction in freshwater consumption and 23.24% savings in the total cost of freshwater. The membrane thermal energy consumption is in the order of 244 &times, 103 kJ/m3 of water, which is found to be less than the range of thermal consumption values reported for membrane distillation in the literature. Although the obtained results are not generally applicable to all shale gas plays, the proposed framework and supporting models aid in understanding the potential impact of using scheduling and optimisation techniques to address flowback wastewater management.

Shale gas production is associated with significant usage of fresh water and discharge of wastewater. Consequently, there is a necessity to create the proper management strategies for water resources in shale gas production and to integrate conventional energy sources (e.g., shale gas) with renewables (e.g., solar energy). The objective of this study is to develop a design framework for integrating water and energy systems including multiple energy sources, cogeneration process, and desalination technologies in treating wastewater and providing fresh water for shale gas production. Solar energy is included to provide thermal power directly to a multi-effect distillation plant (MED) exclusively (to be more feasible economically) or indirect supply through a thermal energy storage system. Thus, MED is driven by direct or indirect solar energy, and excess or direct cogeneration process heat. The proposed thermal energy storage along with the fossil fuel boiler will allow for the dual-purpose system to operate at steady-state by managing the dynamic variability of solar energy. Additionally, electric production is considered to supply a reverse osmosis plant (RO) without connecting to the local electric grid. A multi-period mixed integer nonlinear program (MINLP) is developed and applied to discretize operation period to track the diurnal fluctuations of solar energy. The solution of the optimization program determines the optimal mix of solar energy, thermal storage, and fossil fuel to attain the maximum annual profit of the entire system. A case study is solved for water treatment and energy management for Eagle Ford Basin in Texas.

A wide variety of water sources, treatment methods, and recycling options have created a myriad of water management options. For modeling of sustainable water treatment options, computationally efficient models may be required. This paper provides a comprehensive and comparative review of the water management systems and the associated economic, environmental, and performance metrics. The water management systems are represented as a network of sources, users, technologies, recycling options, and quality of water. Special attention is given to desalination systems. The two main technology categories currently used for desalination are thermal (e.g., Multistage Flash "MSF", Multi-Effect Distillation "MED", and Mechanical Vapor Compression "MVC") and membrane (e.g., seawater reverse osmosis "SWRO", brackish water reverse osmosis "BWRO"). The cost assessment includes a capital cost comparison (for which regression analysis has been used to account for the non-linear nature of the capacity-cost curves), an operating cost comparison, which includes energy requirements, labor costs, chemicals used, maintenance and repair costs, membrane replacement costs and a unit product cost ($/m3) breakdown, which combines the capital and operating costs. Numerous data were collected for the cost of desalination systems. Statistical methods were then used to analyze these collected data to establish deeper understanding of the relationship to capital cost, operating cost, capacity, constraints due to treatment method capabilities, requirements of the users. The paper also briefly discusses other cost considerations such as the water intake and distribution costs. The environmental impacts (concentrate disposal and CO2 footprint) have also been compared for the various technologies considered. Some integration strategies such as use of hybrid systems, cogeneration plants and use of renewable energy have shown reductions in cost associated due to energy consumption and thereby, reducing the unit product cost. Finally, the paper provides a selection guide suitable for various situations with consideration of the different factors affecting cost, environmental impact and energy demands.

Sustainable manufacturing may be defined as the creation of manufactured products using non-polluting, energy and natural resources conserving, and economically sound and safe processes. Recently, there has been renewed interest in United States manufacturing and several industrial and governmental endeavours have been launched for sustainable manufacturing initiatives. To support such initiatives and to prepare the next generation of scientists and engineers, academic institutions have a responsibility to introduce educational programs and tools in the area of sustainable manufacturing. This work provides the engineering community with structured modules for introducing the topic of sustainable manufacturing in the curriculum. Working through an NSF funded SEES RCN program, the SMART CN project delivered 21 educational modules covering various aspects of engineering with the common theme of computer aided process design. This paper summarizes the concepts for module based learning, and a brief review of these educational modules for wider use and dissemination to the academic community.

This work proposes a mathematical optimization model for the design of a biorefining system through an efficient supply chain considering economic (minimization of the total annual cost) and environmental objectives (minimization of the overall CO2 emissions), through the water-energy-food nexus. The proposed model is a mixedinteger linear programming (MILP) problem, which includes the material flow analysis (MFA) technique in order to simulate the changes that take place in a watershed system impacted by the implementation of the biorefining system and determine the water consumption caused mainly by the production of the feedstock and for the operation of the biorefineries. The methodology is configured to account for the uncertainty inherent to prices of feedstocks and products, biofuel demands, biomass requirements, weather conditions, grain production, etc. Finally, a case study located in Mexico is solved for a set of scenarios with the purpose of illustrating the capabilities of the approach. The results show the trade-offs among the considered objectives impacted by the uncertainty in the biorefinery system.

Dry Reforming of Methane (DRM) is one of the CO2 utilization processes in which CO2 reacts with methane to produce syngas (a mixture of CO and H2). The DRM process faces 3 major challenges. Firstly, the highly endothermic nature of DRM (ΔH°298 K = 247 kJ/mol) is much higher than that of the conventional Steam Methane Reforming (SMR) which produces a higher quality syngas (higher H2/CO ratio). Secondly, due to low O/C and H/C ratios in the reaction mixture, catalyst deactivation due to coke formation is a major concern for the DRM process. Thirdly, the syngas ratio (H2/CO ratio) obtained from DRM is ≤ 1. This low syngas ratio is only suited for the production of few chemicals and cannot be extended to Fischer-Tropsch process or petrochemicals like methanol. Despite these obstacles, DRM has received a considerable amount of interest by both academia and industry. Using an Optimization approach, this study aims to better understand the CO2 balance of a process incorporating DRM and compare the performance with existing conventional processes. The objective is to ascertain the regions of operation where DRM helps reduce overall CO2 emissions of syngas production. In the first step of the study, all required data pertaining to CO2 emissions related to existing process was collected from various sources. Emission data for oxidant production (steam, oxygen) and upstream emissions for natural gas were taken GREET® software. In the next step, reformer modeling was performed in LINGO® software. A previously developed Gibbs Free Energy Minimization model [1] has been modified to include fugacity coefficients so that equilibrium calculations can be done at 20 bar pressure. Existing processes considered in the study were Steam Methane Reforming (SMR), Partial Oxidation of Methane (POx) and Auto-Thermal Reformer (ATR). These processes mainly differ in the oxidant/methane ratios and steam-to-carbon ratios used. These constraints have been set to simulate industrial reformer conditions. In the cases which involve DRM, 3 cases were used – stand-alone DRM, DRM+SMR in parallel and DRM+POx in parallel. Though CO2 utilization is the main objective of this study, it is essential to note that an objective function only involving CO2 minimization might result in lower syngas production which is undesirable as it directly reduces plant throughput. Hence, an epsilon method approach was used to iteratively reduce the overall CO2 emissions while maximizing overall syngas production. This method was repeated for each syngas ratio for each reformer case. Results indicate that stand-alone DRM helps achieve a near zero carbon footprint but only at syngas ratio close to 1. Combining equally sized DRM with SMR and POx units does not appreciably reduce overall CO2 emissions. The operating costs of reforming networks involving DRM are highly sensitive to the CO2 purification cost. This assessment shows that incorporating a DRM unit directly into any generic syngas production infrastructure does not have an appreciable reduction in CO2 emissions. Nevertheless, there could still be certain special scenarios where DRM helps in achieving the CO2 reduction objective. To investigate this further, case studies have been used to study the CO2 balance for different processing plants utilizing syngas of different H2/CO ratios. Preliminary findings show that in certain specific regions of operation, DRM assisted reforming helps in overall CO2 reduction objective. The operating cost comparison of these options will be the major criterion which will decide the future of these processing options incorporating DRM. REFERENCES [1] M. M. B. Noureldin, N. O. Elbashir, and M. M. El-Halwagi, “Optimization and selection of reforming approaches for syngas generation from natural/shale gas,” Ind. Eng. Chem. Res., vol. 53, no. 5, pp. 1841–1855, 2014. ACKNOWLEDGMENTS This work was supported by Qatar National Research Fund (QNRF), member of Qatar Foundation (NPRP X – 100 – 2 – 024). The statements made herein are solely the responsibility of the authors. The authors would like to appreciate the continuous support of our industrial partner, Total.

Safety and sustainability are commonly considered once the conceptual design of a process has been carried out. As an effect, modifications to the process to address safety and sustainability issues are more expensive and difficult to make. Several efforts to include safety and sustainability criteria during the process conceptual design have been made. The main challenge is to find a systematic way to reconcile these criteria with the economic objectives to provide an outcome easy to interpret by decision makers. The objective of this paper is to introduce a new safety and sustainability weighted return on investment metric, a tool that is intended to achieve such reconciliation. A case study dealing with the production of methanol from shale gas is presented. The results show how a design with suitable operating conditions that reconcile economic, environmental and safety criteria can be identified with the use of the new metric.

This paper presents a systematic approach to synthesize optimal water distribution networks (WDN) taking into account the water requirement of consumers based on a multi-stakeholder approach. A structural representation is developed to satisfy the water requirements of all users without taking into account water concessions in the region, but considering the depletion of groundwater and surface water, to generate an integral water distribution network. A rigorous mathematical formulation is developed as a mixed-integer linear programming model to transform the water distribution problem into an optimization task that seeks to minimize the overall cost of the water supply network. The proposed optimization model was applied to the Sonoran water scarcity problem from Mexico, where the optimal water distribution network was obtained, based on a multi-criteria approach taking into account the industrial, domestic and agricultural users. The obtained results show that the proposed model can satisfy economic, environmental and social aspects, where the distribution of water is equal for all users without affecting natural resources. Also, the results show economic advantages due to the sales of water.

This aim of this work is to provide a framework for application of Simulated Annealing (SA) Algorithm for solving Water - Heat Nexus problems for Industrial Cities. The combined water-heat network synthesis problem has been formulated into a Mixed Integer Non-Linear Programming (MINLP) and this work describes the various components of the framework needed to handle it. The SA algorithm converges asymptotically across several runs. Apart from the best solution, this tool stores multiple solutions with varying degree of performance. The application of this algorithm has been illustrated with a case study and the benefits of metaheuristics search techniques have been highlighted.

Water and energy consumption has increased substantially over the last decades. Water scarcity has led to an increase in the extraction of fresh water from aquifers, dams and lakes, and it has produced serious overexploitation problems. Furthermore, the population growth in urbanized areas and the increase in water and energy demands in industry, agriculture and households have amplified this problem. As consequence, there are several regions where is almost impossible to satisfy the water demands using the available water resources. In this context, the use of alternative water resources such as reclaimed water, rainwater harvesting and the potential use of desalinated water can be an option. However, desalinated seawater is very expensive because the high energy consumption, and this way to integrate a seawater desalination plant to a power plant to simultaneously produce clean water and power can be an attractive option. This way, this paper proposes an optimization formulation for synthesizing water networks to satisfy water and energy demands in a macroscopic system involving the use of existing water resources and the installation of integrated seawater desalination-power plants. A case study from Mexico (where satisfying the water demands has become a serious problem) is presented. Results show that the integrated system is able to satisfy the current water demands, the excess desalinated water can be used to recharge the overexploited aquifers and interesting profits can be obtained from the sales of power.

This paper introduces an optimization approach to the design of process combined heat and power systems that integrate the thermal profile of the process, an external fossil fuel, and solar energy. A hierarchical design approach is proposed to stage the implementation of steady-state and dynamic calculations. Initially, energy integration is used to identify minimum heating and cooling utility targets. Next, a genetic algorithm approach is employed to optimize the external heating load and generated power of the cogeneration system that includes a steam Rankine cycle. An outer loop is used to optimize the flowrate, temperature, and pressure of the steam entering and exiting the turbine. A multiperiod optimization approach is developed to account for the diurnal variability of solar energy. Direct usage of collected solar energy is considered along with the option of thermal storage and dispatch. The solution of this mixed integer nonlinear program determines the optimal mix of energy throughout the year. A case study for a petrochemical plant in Jeddah, Saudi Arabia was solved to illustrate the applicability of the devised approach.

The sugar cane production is one of the main economic activities of the agriculture sector in several places around the world. Nowadays, the sugarcane production zones face different technologic, economic, and social problems that impact negatively their profitability. The low price of sugar in the market demands the search of alternatives; being the bioethanol production from resides of the sugar cane industry an attractive option. This way, this paper presents a new approach for using the residues from the sugar cane industry to yield a sustainable biorefinery. In this approach, process integration techniques have been implemented to optimize the overall process. A case study from the State of Michoacan in Mexico is presented, where the proposed approach shows significant economic, environmental, and social benefits.

This paper presents a multi-objective optimization approach for designing residential cogeneration systems based on a new superstructure that allows satisfying the demands of hot water and electricity at the minimum cost and the minimum environmental impact. The optimization involves the selection of technologies, size of required units and operating modes of equipment. Two residential complexes in different cities of the State of Michoacan in Mexico were considered as case studies. One is located on the west coast and the other one is in the mountainous area. The results show that the implementation of the proposed optimization method yields significant economic and environmental benefits due to the simultaneous reduction in the total annual cost and overall greenhouse gas emissions.

This paper presents an optimization approach for mitigating CO2 emissions in the electric power generation through integrated algae and cogeneration systems. A framework is proposed for the integration of biofixation of CO2 through the cultivation of microalgae, conversion of microalgae to biodiesel, and a steam power plant with cogeneration that is thermally coupled with an industrial facility. A systematic multi-objective optimization approach is developed to integrate the considered units while simultaneously addressing technical, economic, and environmental objectives. The solution of the optimization problem is carried out via a hierarchical decomposition approach, a genetic algorithm, and the e-constraint method for solving the multi-objective optimization problem. A case study is considered to integrate an existing thermoelectric power station in Mexico with an algae-and-cogeneration system. The results show that important environmental, economic, and energy benefits can be achieved as a result of the proposed integration approach.

Industrial facilities impacting a watershed may be clustered into groups based on their geographical locations. Water usage and discharge for each clustered group of industries may be integrated through the introduction of an eco-industrial park (EIP). This paper presents a mathematical programming model for water integration of EIPs to be synthesized with the purpose of mitigating the environmental impact of industrial effluents discharged into watersheds. The model considers the creation of multiple EIPs, their location, sizing, and tasks. To determine the effect of the discharges on the surrounding watershed, a material flow analysis (MFA) model was coupled with water recycle strategies within the industrial facilities and the associated EIPs. The MFA characterizes the interaction of individual discharges and tracks the impact of the natural (physical, chemical, and biological) phenomena within the watershed on the fate and transport of pollutants. A multiobjective optimization formulation is developed...

This paper presents a novel mathematical formulation for modeling-based optimization of the multiple effect distillation (MED) water desalination process using thermal vapor compression (TVC). The presented model is based on a mass flowrate decoupling concept where flowrates of salt and water in the process are separately considered to allow for the development of a linear programming (LP) optimization formulation while accounting for process heat and mass balances. The system models include stream physical properties that are dependent on temperature, salinity, thermodynamic losses and other properties that are influenced by changes in system pressure such as brine and distillate flashing. The overall modeling approach was used to perform a thermo-economic analysis of the process as well as to identify novel flow routing options for boiler feed water return and de-superheating water sources. A sensitivity analysis was performed to evaluate the effects of various system parameters such as motive steam supply pressure, heating costs, seawater salinity and number of operating effects on the thermo-economically optimal design.

This paper introduces an optimization formulation to design residential water systems that satisfy the water demands in a housing complex involving rainwater harvesting, storage and distribution as well as the simultaneous design of water networks for recycling, reusing, regenerating and storing reclaimed water. The design task is considered as a multi-objective optimization problem where one objective is the minimization of the fresh water consumption and the other objective is the minimization of the total annual cost. The proposed model accounts for the variability in the water demands through the different hours of the day and for the different seasons of the year. The seasonal dependence of the rainwater has also been considered in the optimization model. A case study for the city of Morelia in Mexico is presented. The results show that significant reductions can be obtained in the total fresh water consumption and in the total cost.

Water is one of the most valuable resources in the world and the agriculture is one the largest fresh water consumers due to the low efficiencies in the irrigation processes. This paper proposes a mathematical programming model for the optimal planning of an integrated system which involves water collection, reuse, and distribution strategies. Because of the variability in water supplies and demands throughout the year, a multiperiod optimization approach is adopted. The multi-objective function includes the minimization of fresh water consumption and the minimization of the total annual cost, this cost is divided in capital cost which consists of the catchment areas, storages and pumps, as well as the operating cost for pumping and fresh water. A multi-objective mixed-integer nonlinear programming model is formulated and solved using the ɛ-constrained method. The applicability of the proposed approach was shown through a case study from the State of Michoacan in Mexico where a lot of fresh water is consumed for agricultural purposes. The results show that fresh water consumption can be significantly reduced by the implementation of the proposed approach while simultaneously addressing the economic objective.

Microalgae continues emerging as a promising feedstock for the development of biorefineries. Since there are numerous possible technologies that can be used to process microalgae and produce a wide variety of products and co-products, it is necessary to develop an efficient approach for the generation and screening of processing technologies. This work introduces a combined methodology for the synthesis and analysis of topological pathways for the processing of microalgae based on main approaches of hierarchical and mathematical programming-based process synthesis. The methodology uses a hierarchical approach that starts with top-level data and focuses attention and effort on the promising pathways integrating various process synthesis and optimization concepts such as forward–backward branching, superstructure optimization, and in-depth analysis for high-priority pathways. A case study is solved for the production of diesel-like fuel from microalgae biomass.

A mathematical programming model is presente for the optimal planning of the reuse of municipal solid waste (MSW) to maximize the economic benefit while simultaneously considering sustainability and safety criteria. The proposed methodology considers several phases of the supply chain including waste separation, distribution to processing facilities, processing to obtain useful products, and distribution of products to consumers. Additionally, the safety criteria are based on the potential fatalities associated with waste management. The proposed optimization model is formulated as a multiobjective optimization problem, which considers three different objectives including the maximization of the net annual profit, the maximization of the amount of reused MSW, and the minimization of the social risk associated with the supply chain. The proposed model is applied to a case study in the central-west region of Mexico. The results show the tradeoff between the social risk and the economic and environmental criteria. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1881–1899, 2015

Recently, significant research has been dedicated to the field of mitigating CO2 emissions. Chemical sequestration (fixation) of CO2 into value-added products (e.g., methanol, Fischer–Tropsch liquids, propylene) is an emerging option. The fixation of CO2 via the dry reforming (DR) of natural gas involves the conversion of two greenhouse gases (carbon dioxide and methane) into a useful intermediate (synthesis gas). Synthesis gas can be subsequently converted into various chemicals and fuels. Nevertheless, syngas produced from DR is typically characterized by a H2:CO ratio lower than that typically required for conversion into high-value hydrocarbons. In addition, DR catalysts continuously deactivate as a result of extensive coke formation. This paper focuses on quantifying the potential for CO2 fixation using dry reforming and the integration of different reforming technologies. The results highlight the strong inverse relationship between CO2 chemical fixation and the required syngas H2:CO ratio. Combined...

The efficient use of water worldwide is of overriding importance due to its vital role in life. Recently, several countries have suffered water scarcity mainly due to population increase and problems associated to climate change such as the change in the precipitation patterns in the world. In this project, a mathematical programming model for the efficient and sustainable use of water under parametric uncertainty is proposed. The model considers rainwater harvesting (which includes catchment, storage and distribution) as alternative water source; it also considers sustainability aspects from the economic and environmental points of view, maximizing the revenue from the sales of water minus the cost of production and treatment, while maintaining desirable levels of water in the natural reservoirs. The uncertainty is a result of the change in the precipitation patterns. The proposed model is applied to a case study for the city of Morelia, Michoacan in Mexico, considering a time horizon of 5 years. Results show the optimal schedule for water storage and distribution to different sectors of the society (public, agricultural and industrial users). It was found that the use of alternative water sources such as harvested rainwater, along with an appropriate planning schedule of storage and distribution might help reduce the pressure over natural reservoirs even under conditions of uncertainty in the precipitation, while satisfying the water demands in a city.

This paper presents an optimization approach to the development of macroscopic networks for water integration within industrial cities. The methodology considers various strategies for industrial wastewater reuse amongst different processing facilities that operate within the city. Two different scenarios for the placement of intermediate water treatment interceptors are considered: (1) on-site ‘decentralized’ water treatment within each plant, and (2) off-site ‘centralized’ water treatment that can be shared amongst a cluster of existing industrial plants. The overall objective is to develop cost-efficient water networks that can attain effective interplant water integration scenarios, whilst considerably reducing freshwater consumption and wastewater discharge where appropriate. The optimization model has been formulated as a Mixed Integer Non-Linear Program (MINLP), and accounts for pressure drops associated with piping across all individual source-interceptor-sink allocations. Additionally, a representation that accounts for constrained water transport was adopted, in which designated corridor regions within an industrial city are utilized for the planning of economical pipeline networks that achieve desirable water allocation strategies. In doing so, shortest routing options were obtained between water sources, sinks, and treatment interceptors, according to a given industrial city layout. A case study that considers various different scenarios in terms of contaminant information and piping connectivity is presented to illustrate the proposed approach.

The concept of synthesizing carbon, hydrogen, and oxygen (C-H-O) SYmbiosis Networks (CHOSYNs) for the design of eco-industrial parks is introduced. Within a CHOSYN, compounds containing C-H-O are exchanged, converted, separated, mixed, and allocated. The use of C-H-O as the basis for integration creates numerous opportunities for synergism because C, H, and O are the primary building blocks for many industrial compounds that can be exchanged and integrated. A particularly attractive feature of the CHOSYN framework is its ability to use atomic-based targets to establish benchmarks for the design of macroscopic systems involving multiple processes. Several structural representations, benchmarking, and optimization formulations are developed to embed potential CHOSYN configurations of interest and to synthesize cost-effective networks. A case study with several scenarios is solved to demonstrate the new concept and tools. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1242–1262, 2015

This paper presents an optimization approach for the design of thermal membrane distillation (TMD) systems that are thermally coupled with processing facilities. A superstructure representation and an optimization formulation are introduced to obtain simultaneously the optimization of the TMD unit and the heat-exchange network (HEN) that integrates heating and cooling in the process facility. The superstructure and associated optimization formulation seek to identify the system configuration along with design and operating variables such as heat-exchanger areas, membrane area, extent of thermal coupling between the process and TMD, and the TMD feed-preheating temperature. The objective function maximizes the net annual profit which accounts for the revenues from the sales of purified water, the avoided cost of the treated wastewater, and the total annualized costs accounting for the capital investment of the added heat transfer units and the TMD network, the operating costs for the heating and cooling utilities and the operating expenses for the TMD system. The proposed optimization formulation is applied to a case study where a TMD system is integrated with a methanol plant and the results show significant economic benefits for the implementation of the proposed methodology.

The industrial layout traditionally has been addressed accounting for the facilities distribution and installation since the first day of operation of the plant; this is, without considering future expansions that involve additional facilities in the future operation years. This way, this paper proposes a mathematical programming formulation for the optimal facility sitting and reallocation in an industry accounting for future expansions and involving simultaneously economic and safety objectives. The proposed formulation is based on a multi-annual framework and this corresponds to a multi-objective mixed integer linear programming problem. The proposed optimization approach was applied to a case study for the facility sitting (office buildings and control rooms) in an ethylene oxide plant. The economic objective function involves the minimization of the total annual cost accounting for the value of the money through the time and the safety objective function involves the minimization for the accumulated risk over the operation time. Results show the applicability of the proposed approach.

The field of process integration has gained special importance in the design of chemical processes for its impact in the reduction of energy and fresh water consumption, and in the amount of wastewater generated. Several streams in a given process involve mixtures, and their treatment in integration methods typically assume constant values for their thermodynamic properties, and available mass integration techniques use property operators based on properties of pure compounds and linear mixing rules. Furthermore, the changes of properties in a given unit are typically carried out on an individual basis, neglecting the interactions that may exist with other properties. In this work we report models for the prediction of two properties, kinematic viscosity and thermal conductivity. Kinematic viscosity was chosen because it can be used to follow the interaction between two properties, namely density and dynamic viscosity, and five different models were tested. The model for thermal conductivity included the effect of temperature and composition, for which six different models were tried. Reported experimental data were used to test the models. Based on the resulting models, new functionalities were developed for the implementation of property operators suitable for their application in the design of integration networks.

A model for the synthesis of water networks with a simultaneous integration of energy, mass and properties is presented. The model is formulated within a mixed-integer nonlinear programming framework where the objective function accounts for the minimization for the total annual cost satisfying energy, mass and property constraints for the water streams involved in the network. To accomplish this task, a new superstructure is proposed, in which a first stage for energy integration before mixing streams was considered, followed by a mass and property integration network, and placing finally a second energy integration network. Within this approach, the optimization model identifies when a stream can be used as a hot or a cold stream as part of the energy integration. The proposed approach was applied to two case studies, and the results show that there are significant advantages for the simultaneous implementation of the energy, mass and property integration strategies.

Inherent safety aims to reduce the source of risk in chemical processes. Its application in early design stages represents an advantage due to the flexibility to make changes at that stage of the process development. A relevant application is presented in this work as part of the design of transformation processes for shale gas, since the increasing availability of natural gas due to shale gas extraction has raised the interest in developing processes that use natural gas as a feedstock. Two alternatives for the production of ethylene and four processes for the production of methanol, with natural gas as a feedstock, are analyzed to rank them according to inherent safety levels. The comparison of the technologies is complemented with economic and environmental issues. The results show that from the technologies for ethylene production, oxidative coupling of methane shows better inherent safety levels than the methanol to olefins route, although it offers a low profitability. For the methanol alternatives, the use of steam methane reforming resulted in the safest technology, while partial oxidation offered the highest profit and the autothermal reforming option showed the lowest CO2 emissions. In addition, the most hazardous streams or sections for each process, as provided by the safety indices, were detected.

Mass-exchange operations employ mass-separating agents to induce the transfer of targeted components from the rich phase to the lean phase. Another important class of separations involves the use of energy-separating agents to induce separations. This chapter deals with the optimal design of heat-induced separation networks (HISENs). Examples of HISENs include condensation, crystallization, and drying. The chapter will focus on condensation because of its importance in recovering volatile organic compounds (VOCs), which are among the most serious atmospheric pollutants. A shortcut graphical method will be presented. This method is primarily based on the work of Richburg and El-Halwagi (1995). More generalized procedures and broader applications can be found in the literature.

The overall mass targets identified using the techniques of Chapter 3, Benchmarking Process Performance Through Overall Mass Targeting, can be attained when there are no technical or financial limitations on the solutions. The implementations involve several strategies such as rerouting of streams, manipulation of design and operating variables, addition of new process units, substitution of solvents and chemicals, and alteration of technologies. These strategies provide different levels of cost and benefit. In this chapter, focus is given to direct-recycle networks. Recycle refers to the utilization of a process stream (e.g., a waste or a low-value stream) in a process unit (a sink). While reuse is distinguished from recycle by emphasis that reuse corresponds to the reapplication of the stream for the original intent, we will use the term recycle in a general sense that includes reuse. Specifically, this chapter will focus on direct recycle where the streams are rerouted without the addition of new pieces of equipment. As such, direct recycle is a no/low-cost strategy since it essentially involves pumping and piping and in some cases may even be achieved without the need for additional pumping or piping. In this chapter, a holistic approach is presented for the synthesis of direct-recycle networks with the objective of determining rigorous targets for minimum usage of fresh materials, maximum recycle of process streams, and minimum discharge of waste materials. First, the design problem is stated. Then, a graphical approach (the material-recovery pinch diagram) is described to identify the targets. Next, an algebraic technique is presented to determine the same targets obtained from the graphical approach. Finally, the source-sink mapping diagram is used to identify the detailed rerouting implementations that achieve the targets.

By now (unless you have skipped too many chapters!), you have seen a wide variety of process integration (PI) tools and techniques that systematically provide unique insights, performance benchmarks, and best-in-class implementations. Transforming process integration into value-added applications requires more than just the tools. It requires an effective work process that considers and integrates various technical and business matters. This chapter discusses key items associated with the successful initiation, development, and implementation of successful PI applications.

This work presents an optimization approach to design cogeneration systems able to use flare streams as supplementary fuel during normal and abnormal operation in oil complexes, while the problems associated with flaring are mitigated. A MINLP model is proposed to sizing and designing the cogeneration system that maximizes the net profit and minimizes the greenhouse gas emissions of the system accounting for the uncertain variables associated with abnormal situations and flare streams like flow, frequency, flaring gas quality, and volatility price. This paper proposes a solution approach that enables to determine cumulative probability curves to give to decision makers a useful tool to know the financial risk related to the implementation of the proposed system, and the results show that the proposed system is a suitable option to reduce carbon dioxide emissions and to decrease the operating costs. 1 2017 Elsevier B.V. Scopus

Publisher Summary This chapter provides an overview of the tool of optimization and how it aids in in the systematic solution of process integration problems. Optimization refers to the identification of the “best” solution from among the set of candidate solutions. An optimization formulation typically involves the minimization or maximization of an objective function subject to a number of constraints. Mathematical programming deals with the formulation, solution, and analysis of optimization problems or mathematical programs. The vector x is referred to as the vector of optimization variables, and if it satisfies all the constraints, it is called a feasible point. An optimization problem in which the objective function as well as all the constraints are linear is called a linear program (LP); otherwise, it is termed a nonlinear program (NLP). To formulate an optimization model it is necessary to determine the objective function, develop constraints, and improve formulation. In properly formulating an optimization problem, it is necessary to establish a model that accurately describes the task using mathematical relationships that capture the essence of the problem. When the solution of a mathematical program is implemented using computer-aided tools, it is possible to effectively examine what-if scenarios and conduct sensitivity analysis. LINGO is an optimization software that solves linear, nonlinear, and mixed integer linear and nonlinear programs. If the line for minimizing or maximizing an objective function is not included, LINGO will solve the model as a set of equations provided that the degrees of freedom are appropriate. The use of set formulations is attractive in dealing with large problems with repetitive constraints. The generic set formulation is developed once different scenarios can be run by altering the data.

Carbon Capture and Utilization (CCU) is gaining lot of interest in academia and industry due to the perceived environmental benefits of using CO2 as a feed. Dry Reforming of Methane (DRM) is one such process which uses CO2 and natural gas to produce a valuable intermediate, syngas (mixture of CO and H2). However, to compare DRM with existing reforming technologies (Steam Methane Reforming - SMR and Partial Oxidation of Methane - POx) on an environmental scale, it is important to account for all sources of emissions in each process pathway. This paper discusses an optimization-based approach in LINGO to compare the carbon footprint of different syngas production networks. Reformer outputs have been estimated based on an earlier developed Gibbs energy minimization equilibrium model. The results shows that incorporating a DRM unit will have net CO2 fixation (net reduction in CO2) while producing syngas of low syngas ratios (H2/CO) of 1 and below. However, above syngas ratio of 2, the optimization model does not select DRM in the syngas production network which minimizes overall CO2 emissions.

Dry reforming of methane (DRM) utilizes two greenhouse gases; carbon dioxide (CO2) and methane (CH4) to produce a syngas mixture of carbon monoxide (CO) and hydrogen (H2), which is a very important precursor for the production of a variety of valuable chemicals and liquid fuels. Although DRM offers important advantages for sequestering CO2, its utilization is challenged by certain critical process limitations such as carbon deposition, high energy requirements and low quality of H2:CO ratio. Due to these limitations, DRM process has always been a grey area demanding more research for its successful implementation at an industrial scale. Much of the work recently reported in the literature suggests the utilization of the combination of the conventional reforming technologies like steam reforming (SRM) and partial oxidation (POX) to tackle these limitations. The objective of this work is to develop a process integration approach to the optimization of a process involving DRM, SRM, and POX. As an extension to our previous work involving the optimization of the various operating parameter’s involved in Tri-Reforming (Challiwala et al. 2017), this work is aimed at mass and heat integration of the overall process to determine important benchmarks and to couple the reforming system with the rest of the process. Detailed kinetic and thermodynamic aspects are used to provide a high-fidelity model of the reforming system. The results from the thermodynamic study have been validated by using different combinations of the Langmuir-Hinshelwood-Hougen-Watson (LHHW) based kinetic models pertaining to DRM, POX and SRM processes individually. In particular, the major advancement in this work shows that the combination of individual kinetic models of different systems could be compatible to each other to predict the behaviour of the combined DRM/SRM/POX system and to exploit synergism with mass and energy from the rest of the industrial process. Validation of the results shows excellent agreement between thermodynamic and kinetic product profiles. In addition to this, the combined kinetic model was used to simulate a pseudo-homogeneous fixed bed reformer reactor in MATLAB® and further extended in COMSOL® simulation package to investigate the effect of the transport resistances present in the system under localized conditions of the catalyst bed. The proposed work is carried out at different scales and is conducive to multi-scale system integration and optimization.

Publisher Summary This introductory chapter lays down the foundation for the book, which presents the fundamentals and applications of process integration and how they can be used for generating best-in-class sustainable designs. The foregoing challenges raise the issues of what constitutes a sustainable improvement of the process and how to methodically and efficiently address these challenges. Sustainable designof industrial processes may be defined as the design activities that lead to economic growth, environmental protection, and social progress for the current generation without compromising the potential of future generations to have an ecosystem that meets their needs. It is based on balancing three principal objectives: environmental protection, economic growth, and societal equity. Process integration provides holistic, systematic, generally applicable approaches to benchmark performance ahead of detailed design and then generates best-in-class sustainable design alternatives that reach the desired targets. The objectives of preventing pollution, conserving resources, increasing productivity, and enhancing profitability are among the top priorities of the process industries. The chapter describes several methods for assessing the sustainability of various industrial processes. A central question is not just how to assess sustainability of an industrial process but how to achievea sustainable performance and enhance it. Notwithstanding the numerous design alternatives, process integration can determine the performance target and synthesize the optimal solution without enumeration.

Publisher Summary The synthesis of a network of reactive mass exchangers involves the same challenges as in synthesizing physical MENs. The problem is further compounded by virtue of the reactivity of the MSAs. Typically, reactive MSAs have a greater capacity and selectivity to remove an undesirable component than physical MSAs. Furthermore, because they react with the undesirable species, it may be possible to convert pollutants into other species that may either be reused within the plant itself or sold. The problem of synthesizing reactive mass-exchange networks (REAMENs) was developed, along with systematic techniques for its solution. The first step in synthesizing a REAMEN is to establish the conditions for which the reactive mass exchange is thermodynamically feasible. To establish the conditions for thermodynamic feasibility of reactive mass exchange, it is necessary to invoke the basic principles of mass transfer with chemical reactions. After a procedure for establishing the corresponding composition scales for the rich-lean pairs of streams has been outlined, it is possible to develop the CID. However, it should be noted that the conversion among the corresponding composition scales may be more laborious because of the nonlinearity of equilibrium relations. The necessary thermodynamic concepts are covered in the chapter along with chemical equilibrium, which is tackled in a manner that renders the REAMEN synthesis task close to the MEN problem. Finally, an optimization-based approach is presented and illustrated by a case study.

Heating and cooling are among the most common operations in the process industries. Significant quantities of external heating and cooling utilities are used to drive reaction, induce separation, and render units and streams in desirable states of operation. To satisfy the need for heating and cooling, industrial operations require the extensive use of different forms of energy. For instance, fossil fuels are widely used as feedstocks of boilers to generate heating media (e.g., steam, heating oil) or to drive power cycles that drive refrigeration systems. The excessive usage of external heating utilities incurs a substantial economic burden in the form of operating costs, depletes unsustainable energy resources (e.g., fossil fuels), and generates large quantities of greenhouse gas emissions. Consequently, considerable attention has been given to the problem of conserving energy through heat integration and the synthesis of heat-exchange networks (HENs). The basic idea for heat integration is that there are process streams and units that need to be heated and other process streams and units that need to be cooled. Before using external utilities to provide the necessary heating and cooling, heat integration seeks to transfer the heat from the process hot streams and units to the process cold streams and units. The remaining heating and cooling tasks are then fulfilled using the external heating and cooling utilities. This chapter presents the key tools used in identifying rigorous targets for minimum heating and cooling utilities, selecting utilities, and synthesizing HENs that attain the desired targets.

Publisher Summary This chapter presents a mathematical programming approach to the synthesis of HENs. The HEN formulation is presented and solved to attain minimum heating and cooling utility cost and for selecting the optimum utilities. A formulation is presented to synthesize a network of heat exchangers that can meet the minimum utility targets. After the values of the minimum heating and cooling utilities have been identified, it is desired to synthesize a network of heat exchangers that satisfies the minimum utility targets with the minimum number of heat exchangers. The objective of a minimum number of units is intended to indirectly minimize the fixed cost by assuming that the lower number of units entails lower fixed cost. The nominal designs of HENs and other process subsystems address base-case inputs, outputs, and performance that are set to certain steady-state values. The nominal design can also be changed through retrofitting when a new set of base-case input data and desired outputs are defined. Such retrofitting may include adding new units, increasing the capacities of existing units, rerouting streams, and modifying design and/or operating variables. Scheduling is considered over a certain time horizon during which the operation is discretized into a number of periods. For each period, the HEN synthesis optimization formulation is developed. Then, all these models are combined into a multi-period optimization formulation that accounts for the HEN synthesis over the multiple periods and requires that one configuration be flexible enough to address the scheduling changes.