The industrial sector is a predominant contributor to global energy consumption and carbon emissions.In manufacturing plants,district cooling systems account for a large portion of the energy consumption.However,facil...The industrial sector is a predominant contributor to global energy consumption and carbon emissions.In manufacturing plants,district cooling systems account for a large portion of the energy consumption.However,facility operators often lack understanding of the interactions between water-side systems,distribution piping networks,and demand-side systems(air-side systems and production room)due to the complexities of HVAC control management,manufacturing schedules,and heat flow in and around buildings.This results in poor system operation energy efficiency.Specifically,critical control parameters such as chilled water temperature setpoints and equipment sequencing are frequently managed through empirical settings,leading to inefficiencies as the static operation strategy is difficult to adapt to dynamic operating conditions or actual cooling demands.Therefore,this study proposes a decoupled step-wise simulation-based energy-efficient control method as a dynamic adjustment strategy for industrial space air-conditioning.The method establishes and integrates a Modelica-based water-side system model,distribution system model and room thermal response model to dynamically adjust the operation of pumps,and chillers by predicting the status of the demand-side system under different working conditions.Its main advantage lies in the decoupled and step-wise optimization of subsystems,which enhances interpretability,computability and scalability for practical deployment in industrial scenarios.A case study at a large-scale manufacturing plant showed that by applying the dynamic adjustment strategy,this method can contribute to reducing system energy consumption by around 23%during typical cooling seasons compared to the baseline rule-based control,demonstrating the significant potential of the proposed method for improving energy efficiency.展开更多
基金supported under the project of Research and Demonstration of Key Technologies for Low-Carbon Design and Optimization of Community(Park)Regional Integrated Energy Systems,by the Ningbo Science and Technology Bureau Major Science and Technology Programme,with project code 2022Z161.
文摘The industrial sector is a predominant contributor to global energy consumption and carbon emissions.In manufacturing plants,district cooling systems account for a large portion of the energy consumption.However,facility operators often lack understanding of the interactions between water-side systems,distribution piping networks,and demand-side systems(air-side systems and production room)due to the complexities of HVAC control management,manufacturing schedules,and heat flow in and around buildings.This results in poor system operation energy efficiency.Specifically,critical control parameters such as chilled water temperature setpoints and equipment sequencing are frequently managed through empirical settings,leading to inefficiencies as the static operation strategy is difficult to adapt to dynamic operating conditions or actual cooling demands.Therefore,this study proposes a decoupled step-wise simulation-based energy-efficient control method as a dynamic adjustment strategy for industrial space air-conditioning.The method establishes and integrates a Modelica-based water-side system model,distribution system model and room thermal response model to dynamically adjust the operation of pumps,and chillers by predicting the status of the demand-side system under different working conditions.Its main advantage lies in the decoupled and step-wise optimization of subsystems,which enhances interpretability,computability and scalability for practical deployment in industrial scenarios.A case study at a large-scale manufacturing plant showed that by applying the dynamic adjustment strategy,this method can contribute to reducing system energy consumption by around 23%during typical cooling seasons compared to the baseline rule-based control,demonstrating the significant potential of the proposed method for improving energy efficiency.
