Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively

A Systematic Approach for Designing Industrial Park Integration Networks Across the Water-Energy Nexus

Nowadays, water-energy resource faces growing demands and constraints in many regions as a result of economic, population growth and climate change. The water-energy nexus and integration has been recently proposed to minimize water-energy footprint of an industrial park. It is required to develop a systematic approach for water-energy network and interconnections among the processes. Previous research work has presented the general superstructure and approach to develop economically optimal water networks that achieve a specified footprint target. In this work, the previous approach for water network has been extended with cooling systems options in order to capture the linkages between water and energy within industrial cities. The objective of this paper is to develop a framework for optimizing energy and water resources from processes that have a surplus of energy at various qualities. A systematic procedure is developed for optimizing and maximizing the benefits of these nexuses, considering power generation from a net surplus of waste heat energy from each plant by accounting for different sustainability metrics. The developed approach includes the use of composite curve analysis to first identify the potential for excess heat and then used to develop the combined water-energy network. A superstructure is generated to embed various configurations and related optimization formulation is solved to obtain an optimal process that economically satisfies the demand for water and energy considering some environmental metrics. Special emphasis is placed on capturing the synergy potentials from utilizing excess process heat and synergies across cooling and desalination systems, as well as synergies with the surroundings in terms of power and water exports from the industrial cluster. The work considers multiple objectives to explore trade-offs between minimum total annual cost and environmental sustainability metrics. A case study of an industrial cluster of typical processes operating in Qatar is presented to highlight the benefits of integration. It is shown how economically very attractive solutions across the nexus are identified by the proposed optimization-based approach. The results indicate that by water-energy integration the footprint reduction can be significant while economically is attractive too. Therefore, there is a great potential for savings water-energy resources by water-energy integrations. The work is contributed to sustainable development such as less pollution and resource minimization

Wastewater minimization in multipurpose batch processes using mathematical modelling

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, in fulfillment of the requirements for the degree of Master of Science in Engineering May 2018The increase in the degradation of water sources and stringent environmental regulations have greatly motivated industries to explore means of utilizing water efficiently. Batch processes are known to generate highly contaminated wastewater that is toxic to the environment. A holistic approach to design which emphasizes the unity of the process, process integration (PI), can be used to reduce both the wastewater generated and the level of contamination while maintaining the profitability of the chemical plant. Process integration techniques for wastewater minimization in batch processes include water reuse, recycle and regeneration. Most mathematical formulations for wastewater minimization in multipurpose batch processes presented in literature determine the amount of water required for washing operations by only looking at the task that has just occurred in a unit. However, the nature of the succeeding task can influence the amount of water required for the washing operation between consecutive tasks in a processing unit. In paint manufacturing, for example, more water will be required for the washing operation if the production of white paint follows the production of black paint and less water will be required if the black paint follows the white paint. The amount of wastewater generated in batch processes can, therefore, be reduced by simply synthesizing a sequence of tasks that will generate the least amount of wastewater. Presented in this work are wastewater minimization formulations for multipurpose batch processes which explore sequence dependent changeover opportunities for water minimization simultaneously with direct and indirect water reuse and recycle opportunities. The presence of continuous and integer variables, as well as bilinear terms, rendered the model a Mixed Integer Nonlinear Program (MINLP). The developed MINLP model was validated using two single contaminant illustrative examples and a multiple contaminant example. A global optimization solver, Branch and Reduce Optimization Navigator (BARON), was used to solve the optimization problems on a General Algebraic Modeling System (GAMS) platform. Exploring multiple water saving opportunities simultaneously has proven to be computationally intensive but can result in significant water savings. For instance, two different scenarios saved 65% and 61% in freshwater use respectively.MT 201

Industrial water management by multiobjective optimization: from individual to collective solution through eco-industrial parks.

Industrial water networks are designed in the first part by a multiobjective optimization strategy, where fresh water, regenerated water flow rates as well as the number of network connections (integer variables) are minimized. The problem is formulated as a Mixed-Integer Linear Programming problem (MILP) and solved by the ε-constraint method. The linearization of the problem is based on the necessary conditions of optimality defined by Savelski and Bagajewicz (2000). The approach is validated on a published example involving only one contaminant. In the second part the MILP strategy is implemented for designing an Eco-Industrial Park (EIP) involving three companies. Three scenarios are considered: EIP without regeneration unit, EIP where each company owns its regeneration unit and EIP where the three companies share regeneration unit(s). Three possible regeneration units can be chosen, and the MILP is solved under two kinds of conditions: limited or unlimited number of connections, same or different gains for each company. All these cases are compared according to the global equivalent cost expressed in fresh water and taking also into account the network complexity through the number of connections. The best EIP solution for the three companies can be determined

Superstructure optimisation of a water minimisation network with a embedded multicontaminant electrodialysis model

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering, 2016The water-energy nexus considers the relationship between water and energy resources. Increases in environmental degradation and social pressures in recent years have necessitated the development of manufacturing processes that are conservative with respect to both these resources, while maintaining financial viability. This can be achieved by process integration (PI); a holistic approach to design which emphasises the unity of processes. Within the realm of PI, water network synthesis (WNS) explores avenues for reuse, recycle and regeneration of effluent in order to minimise freshwater consumption and wastewater production. When regeneration is required, membrane-based treatment processes may be employed. These processes are energy intensive and result in a trade-off between water and energy minimisation, thus creating an avenue for optimisation. Previous work in WNS employed a black box approach to represent regenerators in water minimisation problems. However, this misrepresents the cost of regeneration and underestimates the energy requirements of a system. The aim of the research presented in this dissertation is to develop an integrated water regeneration network synthesis model to simultaneously minimise water and energy in a water network. A novel MINLP model for the design of an electrodialysis (ED) unit that is capable of treating a binary mixture of simple salts was developed from first principles. This ED model was embedded into a water network superstructure optimisation model, where the objective was to minimise freshwater and energy consumption, wastewater productions, and associated costs. The model was applied to a pulp and paper case study, considering several scenarios. Global optimisation of the integrated water network and ED design model, with variable contaminant removal ratios, was found to yield the best results. A total of 38% savings in freshwater, 68% reduction in wastewater production and 55% overall cost reduction were observed when compared with the original design. This model also led to a 80% reduction in regeneration (energy) cost.GS201

A contribution to support decision making in energy/water sypply chain optimisation

The seeking of process sustainability forces enterprises to change their operations. Additionally, the industrial globalization implies a very dynamic market that, among other issues, promotes the enterprises competition. Therefore, the efficient control and use of their Key Performance Indicators, including profitability, cost reduction, demand satisfaction and environmental impact associated to the development of new products, is a significant challenge. All the above indicators can be efficiently controlled through the Supply Chain Management. Thus, companies work towards the optimization of their individual operations under competitive environments taking advantage of the flexibility provided by the virtually inexistent world market restrictions. This is achieved by the coordination of the resource flows, across all the entities and echelons belonging to the system network. Nevertheless, such coordination is significantly complicated if considering the presence of uncertainty and even more if seeking for a win-win outcome. The purpose of this thesis is extending the current decision making strategies to expedite these tasks in industrial processes. Such a contribution is based on the development of efficient mathematical models that allows coordinating large amount of information synchronizing the production and distribution tasks in terms of economic, environmental and social criteria. This thesis starts presents an overview of the requirements of sustainable production processes, describing and analyzing the current methods and tools used and identifying the most relevant open issues. All the above is always within the framework of Process System Engineering literature. The second part of this thesis is focused in stressing the current Multi-Objective solution strategies. During this part, first explores how the profitability of the Supply Chain can be enhanced by considering simultaneously multiple objectives under demand uncertainties. Particularly, solution frameworks have been proposed in which different multi-criteria decision making strategies have been combined with stochastic approaches. Furthermore, additional performance indicators (including financial and operational ones) have been included in the same solution framework to evaluate its capabilities. This framework was also applied to decentralized supply chains problems in order to explore its capabilities to produce solution that improves the performances of each one of the SC entities simultaneously. Consequently, a new generalized mathematical formulation which integrates many performance indicators in the production process within a supply chain is efficiently solved. Afterwards, the third part of the thesis extends the proposed solution framework to address the uncertainty management. Particularly, the consideration of different types and sources of uncertainty (e.g. external and internal ones) where considered, through the implementation of preventive approaches. This part also explores the use of solution strategies that efficiently selects the number of scenarios that represent the uncertainty conditions. Finally, the importance and effect of each uncertainty source over the process performance is detailed analyzed through the use of surrogate models that promote the sensitivity analysis of those uncertainties. The third part of this thesis is focused on the integration of the above multi-objective and uncertainty approaches for the optimization of a sustainable Supply Chain. Besides the integration of different solution approaches, this part also considers the integration of hierarchical decision levels, by the exploitation of mathematical models that assess the consequences of considering simultaneously design and planning decisions under centralized and decentralized Supply Chains. Finally, the last part of this thesis provides the final conclusions and further work to be developed.La globalización industrial genera un ambiente dinámico en los mercados que, entre otras cosas, promueve la competencia entre corporaciones. Por lo tanto, el uso eficiente de las los indicadores de rendimiento, incluyendo rentabilidad, satisfacción de la demanda y en general el impacto ambiental, representa un area de oportunidad importante. El control de estos indicadores tiene un efecto positivo si se combinan con la gestión de cadena de suministro. Por lo tanto, las compañías buscan definir sus operaciones para permanecer activas dentro de un ambiente competitivo, tomando en cuenta las restricciones en el mercado mundial. Lo anterior puede ser logrado mediante la coordinación de los flujos de recursos a través de todas las entidades y escalones pertenecientes a la red del sistema. Sin embargo, dicha coordinación se complica significativamente si se quiere considerar la presencia de incertidumbre, y aún más, si se busca exclusivamente un ganar-ganar. El propósito de esta tesis es extender el alcance de las estrategias de toma de decisiones con el fin de facilitar estas tareas dentro de procesos industriales. Estas contribuciones se basan en el desarrollo de modelos matemáticos eficientes que permitan coordinar grandes cantidades de información sincronizando las tareas de producción y distribución en términos económicos, ambientales y sociales. Esta tesis inicia presentando una visión global de los requerimientos de un proceso de producción sostenible, describiendo y analizando los métodos y herramientas actuales así como identificando las áreas de oportunidad más relevantes dentro del marco de ingeniería de procesos La segunda parte se enfoca en enfatizar las capacidades de las estrategias de solución multi-objetivo, durante la cual, se explora el mejoramiento de la rentabilidad de la cadena de suministro considerando múltiples objetivos bajo incertidumbres en la demanda. Particularmente, diferentes marcos de solución han sido propuestos en los que varias estrategias de toma de decisión multi-criterio han sido combinadas con aproximaciones estocásticas. Por otra parte, indicadores de rendimiento (incluyendo financiero y operacional) han sido incluidos en el mismo marco de solución para evaluar sus capacidades. Este marco fue aplicado también a problemas de cadenas de suministro descentralizados con el fin de explorar sus capacidades de producir soluciones que mejoran simultáneamente el rendimiento para cada uno de las entidades dentro de la cadena de suministro. Consecuentemente, una nueva formulación que integra varios indicadores de rendimiento en los procesos de producción fue propuesta y validada. La tercera parte de la tesis extiende el marco de solución propuesto para abordar el manejo de incertidumbres. Particularmente, la consideración de diferentes tipos y fuentes de incertidumbre (p.ej. externos e internos) fueron considerados, mediante la implementación de aproximaciones preventivas. Esta parte también explora el uso de estrategias de solución que elige eficientemente el número de escenarios necesario que representan las condiciones inciertas. Finalmente, la importancia y efecto de cada una de las fuentes de incertidumbre sobre el rendimiento del proceso es analizado en detalle mediante el uso de meta modelos que promueven el análisis de sensibilidad de dichas incertidumbres. La tercera parte de esta tesis se enfoca en la integración de las metodologías de multi-objetivo e incertidumbre anteriormente expuestas para la optimización de cadenas de suministro sostenibles. Además de la integración de diferentes métodos. Esta parte también considera la integración de diferentes niveles jerárquicos de decisión, mediante el aprovechamiento de modelos matemáticos que evalúan lasconsecuencias de considerar simultáneamente las decisiones de diseño y planeación de una cadena de suministro centralizada y descentralizada. La parte final de la tesis detalla las conclusiones y el trabajo a futuro necesario sobre esta línea de investigaciónPostprint (published version

Systematic Design, Optimization, and Sustainability Assessment for Generation of Efficient Wastewater Treatment Networks

Due to population growth and economic development, there has been an increase in global wastewater (WW) generation footprint. There are different technologies associated with the wastewater treatment (WWT) process. The challenge is to select technologies that minimize the cost of treatment, as well as meet purity requirements. Further, there is a need to integrate sustainability analysis to facilitate a holistic decision. With the application of systems engineering, sustainable and cost-effective solutions can be achieved. In this work, we apply systems engineering to generate a sustainable and cost-effective solution. A superstructure was generated by categorizing technologies into four treatment stages. After modeling all functional equations for each technology, an optimization problem was formulated to determine the best path for the treatment process. Mixed-integer non-linear programming (MINLP), which implements a 0–1 binary integer constraint for active/inactive technologies at each stage was used. Sustainability analysis was performed for each representative case study (municipal and pharmaceutical WWT) using the sustainable process index (SPI). The total cost of municipal WWT is 1.92 USD/m3, while that for the pharmaceutical WWT is 3.44 USD/m3. With the treatment of WW, there is a reduction of over 90% ecological burden based on the SPI metric