Periodic Reporting for period 1 - DYNCON-ORC (Dynamic performance modelling and controller design of a mini-scale organic Rankine cycle unit for heavy duty vehicles)
Période du rapport: 2017-05-01 au 2019-04-30
Internal combustion engines of heavy duty vehicles convert only approximately 40 % of the combustion heat to mechanical power, while rest of the heat is rejected into the environment as waste heat. An organic Rankine cycle (ORC) unit that recovers part of this waste heat can significantly reduce the fuel consumption and carbon dioxide emissions of heavy duty transportation. This project addresses the dynamic modelling and control aspects of the ORC system for waste heat recovery from heavy duty vehicles. To this end, a non-linear model predictive controller has been developed for organic Rankine cycle for waste heat recovery from heavy duty vehicles in this project.
The research results have demonstrated that the dynamic model can predict the dynamic response of the ORC system within reasonable accuracy compared with experimental data. The selection of the heat exchanger modelling approach in the dynamic model of ORC system significantly effects the computational time and accuracy of the results. The results suggest that the finite volume approach for dynamic modelling of heat exchanger with appropriate heat transfer and pressure drop correlations outperforms the moving boundary layer in terms of computational time and accuracy to predict the dynamic response. The non-linear model predictive controller has effectively rejected the disturbance caused by the exhaust waste heat while maintaining the optimum operating condition. The non-linear model predictive controller has outperformed the traditional proportional–integral–derivative controller in terms of response time and power output of the ORC system.
The research results have demonstrated that the dynamic model can predict the dynamic response of the ORC system within reasonable accuracy compared with experimental data. The selection of the heat exchanger modelling approach in the dynamic model of ORC system significantly effects the computational time and accuracy of the results. The results suggest that the finite volume approach for dynamic modelling of heat exchanger with appropriate heat transfer and pressure drop correlations outperforms the moving boundary layer in terms of computational time and accuracy to predict the dynamic response. The non-linear model predictive controller has effectively rejected the disturbance caused by the exhaust waste heat while maintaining the optimum operating condition. The non-linear model predictive controller has outperformed the traditional proportional–integral–derivative controller in terms of response time and power output of the ORC system.
The work performed during the project include:
• Development of project plan and project management
• Preliminary design of the ORC system based on transient heat source
• Development of 1-dimensional steady state model of the ORC system
• Optimization of the ORC system for to minimize the weight, volume and cost of the ORC system and maximize the power output of the ORC system
• Design and development of the ORC experimental test rig
• Component specification and selection for ORC experimental test rig
• Development and validation of the dynamic model of the ORC system
• Development of proportional–integral–derivative controller of the ORC system
• Design and development of the non-linear model predictive controller of the ORC system
• Performance evaluation and comparison of the non-linear model predictive controller and proportional–integral–derivative controller
• Training activities including optimization, dynamic modelling and model predictive controller
• Presentation of project results at international conferences
• Preparation and submission of the journal articles for dissemination activities
• Supervision of the undergraduate and graduate students
• Outreach activities through participation in European Researcher night, Marie Curie Alumni Association, and Project Open Day
• Postgraduate course in Model Predictive Control
• Project coordination and meetings among the project partners
The result and outcomes of the project include:
• 1-D steady state model and optimized design of mini organic Rankine cycle
• Component level dynamic model of the organic Rankine cycle unit
• Optimized non-linear model predictive controller for organic Rankine cycle unit
The outcomes and results has been disseminated through conference publication and journal publications. The outcomes of the results has been transferred to industry. The outcomes of the project contribute to resolving one of the major technical barriers for successful commercialization of the ORC system for waste heat recovery from internal combustion engine of heavy duty vehicles.
• Development of project plan and project management
• Preliminary design of the ORC system based on transient heat source
• Development of 1-dimensional steady state model of the ORC system
• Optimization of the ORC system for to minimize the weight, volume and cost of the ORC system and maximize the power output of the ORC system
• Design and development of the ORC experimental test rig
• Component specification and selection for ORC experimental test rig
• Development and validation of the dynamic model of the ORC system
• Development of proportional–integral–derivative controller of the ORC system
• Design and development of the non-linear model predictive controller of the ORC system
• Performance evaluation and comparison of the non-linear model predictive controller and proportional–integral–derivative controller
• Training activities including optimization, dynamic modelling and model predictive controller
• Presentation of project results at international conferences
• Preparation and submission of the journal articles for dissemination activities
• Supervision of the undergraduate and graduate students
• Outreach activities through participation in European Researcher night, Marie Curie Alumni Association, and Project Open Day
• Postgraduate course in Model Predictive Control
• Project coordination and meetings among the project partners
The result and outcomes of the project include:
• 1-D steady state model and optimized design of mini organic Rankine cycle
• Component level dynamic model of the organic Rankine cycle unit
• Optimized non-linear model predictive controller for organic Rankine cycle unit
The outcomes and results has been disseminated through conference publication and journal publications. The outcomes of the results has been transferred to industry. The outcomes of the project contribute to resolving one of the major technical barriers for successful commercialization of the ORC system for waste heat recovery from internal combustion engine of heavy duty vehicles.
The progress of the project beyond the state of the art include the following findings: 1) the size of the components, especially the heat exchanger plays an important role in dynamic modelling and control of the ORC system. For highly transient heat sources, the dynamic modelling and control aspects should be incorporated in the preliminary design; 2) the non-linear model predictive controller outperforms the conventional proportional–integral–derivative controller; 3) implementation of an optimized non-linear model predictive control based ORC system for long haul truck can reduce the fuel consumption by 14%.
Development of the dynamic model and optimize controller will lead to improvements in overall system efficiency of the ORC system for waste heat recovery from internal combustion of heavy duty vehicles, and increase the uptake of low carbon technologies in transportation sector. This will lead to reductions in CO2 emissions, resulting in a positive impact on the climate whilst reducing the fuel consumption. In broader terms, the project will contribute to the development of a more efficient energy system for vehicles, reducing the fuel consumption and carbon dioxide emissions of the transportation sector, thus helping to attain socio-economic and environmental targets. The successful commercialization of the ORC system for waste heat recovery from internal combustion of heavy duty vehicles will furthermore create new business and job opportunities.
The development of safe and efficient controller will lead to improvements in overall system efficiency of the ORC system. This will lead to reductions in CO2 emissions from transportation sector. Furthermore, developing expert knowledge in this field will lead the way for future commercialisation, stimulating investment and creating new jobs and businesses within the energy sector.
Development of the dynamic model and optimize controller will lead to improvements in overall system efficiency of the ORC system for waste heat recovery from internal combustion of heavy duty vehicles, and increase the uptake of low carbon technologies in transportation sector. This will lead to reductions in CO2 emissions, resulting in a positive impact on the climate whilst reducing the fuel consumption. In broader terms, the project will contribute to the development of a more efficient energy system for vehicles, reducing the fuel consumption and carbon dioxide emissions of the transportation sector, thus helping to attain socio-economic and environmental targets. The successful commercialization of the ORC system for waste heat recovery from internal combustion of heavy duty vehicles will furthermore create new business and job opportunities.
The development of safe and efficient controller will lead to improvements in overall system efficiency of the ORC system. This will lead to reductions in CO2 emissions from transportation sector. Furthermore, developing expert knowledge in this field will lead the way for future commercialisation, stimulating investment and creating new jobs and businesses within the energy sector.