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Maheshwari, Abhilasha
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Maheshwari, Abhilasha
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Maheshwari, A.
Maheshwari A.
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6 results
Now showing 1 - 6 of 6
- PublicationStochastic Optimization Model for Short-term Planning of Tanker Water Supply Systems in Urban Areas(2022-01-01)
; ;Misra, S. ;Gudi, R. ;Subbiah, S.Laspidou, C.Tanker-based water distribution systems are amongst one of the most prevalent methods to supply water in many developing countries facing water crisis and intermittent piped water supply. These tanker water supply systems need tighter coordination between the water sources, treatment facilities, consumers and tanker suppliers to efficiently manage timely delivery and quality water. Furthermore, accounting for the uncertain nature of customer demands while making the planning decisions would not only aid in achieving minimum operational cost but is also important in reducing the water wastage. This paper proposes a two-stage stochastic recourse programming model for an optimal planning and scheduling of tanker water supply system under daily demand uncertainty. The main objective is to supply water to maximum number of consumers with minimum total operating costs. A solution strategy combining Sample Average Approximation (SAA) and Monte-Carlo Simulation (MCS) methods, to generate an equivalent deterministic MILP (mixed integer programming problem) model with multiple scenarios of demand uncertainty realization, is adopted for problem solving. The proposed model is applied to an example tanker water supply system and the benefits of two-stage stochastic modelling in making agile decisions incorporating the effect of uncertainties are illustrated. The results also demonstrate the efficacy of adopting stochastic programming models and methods in such real-world application cases. - PublicationOptimal energy storage system design for addressing uncertainty issues in integration of supply and demand-side management approaches(2024-06-01)
;Misra, Shamik; Gudi, Ravindra D.The primary goal of Sustainable Development Goal 7 (SDG 7) is to increase renewable energy use to reduce reliance on fossil fuels and mitigate climate change. Energy-intensive industries can benefit from in-house renewable power generation, reducing their reliance on fossil fuel-based grid power and making processes greener. However, integration among power generation/purchase, energy storage systems (ESS), and power consumption is crucial to overcome the intermittent nature of renewable power sources. ESS plays a vital role in increasing resilience and optimizing material production based on power availability and pricing. The efficacy of ESS needs critical evaluation considering cost, efficiency, and uncertainties related to renewable power generation and material product demand. The paper proposes two scenario-based optimization approaches to assess the impact of uncertainties on the integrated supply and demand side management (ISDM) system, focusing on lithium-ion batteries and cryogenic energy storage (CES). Compared to the conservative approach of the scenario-based robust optimization (SRO) method, the proposed stochastic simulation optimization (SSO) method provides a ‘risk-neutral’ solution, which is 6.45% less than the minimum expected cost solution. The analysis also suggests that lithium-ion batteries are more economically effective for the proposed integrated framework than CES, resulting in almost a 29% reduction in operating costs compared to no battery option. The proposed framework could contribute to sustainable and economically viable energy management practices in energy-intensive industries. Further research and implementation of such frameworks could accelerate the adoption of renewable energy and energy storage technologies in industrial processes. - PublicationAn EPANET - MATLAB Framework for Quality and Quantity Management in Intermittent Water Supply Network(2022-01-01)
;Bharucha, Aadil; Gudi, Ravindra D.In most developing countries while water supply is intermittent, maintaining the adequate water quality in such a network is a major challenge due to presence of supply and non-supply hours schedules as well as pressure transients during startup and shut down of the water supply. Water quality analysis tools and optimization techniques need to be modified and adapted for sustainable water quality and quantity management. This study combines the well-known EPANET and MATLAB platforms to develop a framework for generating optimal disinfectant dosage policy in an intermittent water network, while maintaining the residual chlorine concentration within the acceptable global drinking water standards throughout the network all time. The aim of this paper is to develop a framework to study the effect of network characteristics and evolve effective policies for water management in such water distribution network. - PublicationDigital twin assisted decision support system for quality regulation and leak localization task in large-scale water distribution networks(2023-12-01)
;Brahmbhatt, Parth; Gudi, Ravindra D.Effective water resource management is essential in large metropolitan cities. Digital Twins (DT), supported by IIoT and machine learning technologies, provide opportunities for real-time prediction and optimization for effective decision-making in water distribution systems. A framework for the digital twin of the Water Distribution Network (WDN) is developed in this paper to achieve higher operational efficiency using ‘WNTR’, the Python-based library of EPANET. All computational experiments and methods were validated on the benchmark hydraulic C-TOWN network (Ostfeld et al., 2011). The hydraulic parameters and quality parameters of the DT model for the water network were calibrated using the Differential Evolution (DE) algorithm. The calibrated DT served as a real-time proxy to generate simulation data, which is used for two different applications in large-scale water networks: (i) Disinfectant dosage regulation task using booster stations and (ii) pipe leakage localization task. The calibrated DT was utilized to estimate the optimal disinfectant dosing rates, ensuring water quality control within an acceptable range using optimization. The results highlight the effectiveness of the neural network and real-time optimization strategy to achieve the optimal dosing rate. For the leakage localization task, the Graph Convolution Networks (GCN) based neural network trained on the DT was found to predict leakage location very accurately. - PublicationAn Operational Scheduling Framework for Tanker-Based Water Distribution Systems under Uncertainty(2023-07-12)
; ;Misra, Shamik ;Gudi, Ravindra ;Subbiah, SenthilmuruganLaspidou, ChrysiTanker water systems play a critical role in providing adequate service to meet potable water demands in the face of acute water crisis in many cities globally. Managing tanker movements among the supply and demand sides requires an efficient scheduling framework that could promote economic feasibility, ensure timely delivery, and avoid water wastage. However, to realize such a sustainable water supply operation, inherent uncertainties related to consumer demand and tanker travel time need to be accounted in the operational scheduling. Herein, a two-stage stochastic optimization model with a recourse approach is developed for scheduling and optimization of tanker-based water supply and treatment facility operations under uncertainty. The uncertain water demands and tanker travel times are combinedly modeled in a computationally efficient manner using a hybrid Monte Carlo simulation and scenario tree approach. The maximum demand fulfillment, limited extraction of groundwater, and timely delivery of quality water are enforced through a set of constraints to achieve sustainable operation. A representative urban case study is demonstrated, and results are discussed for two uncertainty cases: (i) only demand and (ii) integrated demand-travel time. The value of stochastic solutions over the expected value and perfect information model solutions are analyzed and features of the framework for informed decision-making are discussed.Scopus© Citations 1 - PublicationTowards achieving SDG-6 in steel industry: A superstructure optimization-based approach(2023-10-01)
;Bharucha, Aadil; ;Prasad, VijaysaiGudi, Ravindra D.The increasing water scarcity and stringent norms on environmental pollution control impose a demand on the development of sustainable methods for water use in process operations. The steel industry is one such highly water-intensive industry with a water requirement of 2–4 m3/ton of steel and a production of 1950 million tons in 2021 globally. Optimum water network synthesis by recycling, regeneration, and reuse of water, is one of the ways of sustainable management of water in industries. This study explores several opportunities for water network optimization in the steel industry, through appropriate water flow targeting. The opportunities for direct reuse of blowdown water, or water reuse after regeneration of blowdown and process loss water as makeup water, are explored. These opportunities are evaluated algorithmically using a superstructure-based optimization framework. Realistic operational constraints such as the hardness of makeup water and the Langelier Saturation Index (LSI) value of blowdown water are also incorporated in the developed formulation. The interplay between the cycles of concentration (CoC) and blowdown water quality is also studied, first with a representative example problem and then on a representative steel complex network. The advantages of inter-plant water reuse over intra-plant water reuse and the effect of pumping cost on the optimized water network are demonstrated. The result shows that operating cost and freshwater requirement can be reduced to around 50 % by inter-plant reuse and regeneration of blowdown and process loss water through appropriate recycle streams, and by optimizing the operating CoC of the cooling towers in the cooling systems. It is also observed that the cooling towers should be operated locally at the resultant optimal CoC as compared to the conventional method of operating all at the highest possible blowdown concentration to achieve maximum freshwater savings in the water system.Scopus© Citations 1