A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty

Abstract

Discrepancies between water demand and supply are intensifying and creating a need for sustainable water resource process management associated with rapid economic development, population growth, and urban expansion. In this study, a scenario-based interval fuzzy-credibility constrained programming (SIFCP) method is developed for planning a water resource management system (WRMS) that can handle uncertain information by using interval values, fuzzy sets, and scenario analysis. The SIFCP-WRMS model is then applied to plan the middle route of the South-to-North Water Diversion Project (SNWDP) in Henan Province, China. Solutions of different water distribution proportion scenarios and varied credibility levels are considered. Results reveal that different water-distribution proportion scenarios and uncertainties used in the SIFCP-WRMS model can lead to changed water allocations, sewage discharges, chemical oxygen demand (COD) emissions, and system benefits. Results also indicate that the variation of scenarios (i.e., from S2 to S3) can result in a change of 9% over the planning horizon for water allocation in the industrial sector. Findings can help decision-makers resolve conflicts among economic objective, water resource demand, and sewage discharge, as well as COD emissions.

Special Issue on “Energy, Economy and Environment for Industrial Production Processes”

“…Their main findings are discussed and presented by emphasizing the effects of several parameters on the system's performance. Miao et al [28] present a scenario-based optimization model for planning sustainable water-resource process management under uncertainty. Their results reveal that different water-distribution proportion scenarios and uncertainties can lead to changed water allocations, sewage discharges, chemical oxygen demand (COD) emissions and system benefits, and the variation of scenarios (i.e., from S2 to S3) can result in a change of 9% over the planning horizon for water allocation in the industrial sector.…”

Data-Driven Robust Optimization for Steam Systems in Ethylene Plants under Uncertainty

“…Epigraph re f ormulation o f ob jective f unction (13) Mass balance and energy balance constraints (1)-(3) Sha f t power demand constraint (6) Process demand constraints (7), (14) Variables range constraints (8), (15), (16) (DDROSS)…”

“…Research [13] proposed a worst-case optimization approach for real-time optimization of a gas lifted well system. Research [14] presented a fuzzy-credibility constrained programming approach for water resource planning under uncertainty. Research [15] utilized stochastic programming to handle uncertainties in the site utility system.…”

CWM-CGAN Method for Renewable Energy Scenario Generation Based on Weather Label Multi-Factor Definition

“…One is the scenario method [17,18], which simulates and generates scenario data that are close to history by analyzing the characteristics of historical data, such as establishing a probability distribution function or learning data characteristics through the artificial intelligence method. The second is the quantitative analysis method [19,20], which defines the uncertain specific values of the interval method or matrix method. For the planning and operation of energy system, the advantage of scenario method is that the final results can be obtained by substituting different scenarios into the calculation, and the results can be easily understood through a simulation method.…”