Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consum...Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control(CMPC)strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.展开更多
Organic Rankine Cycles(ORCs) are an effective way to produce electricity from low-grade heat sources, which cannot be effectively obtained using conventional high-temperature Rankine cycles. Due to the lack of availab...Organic Rankine Cycles(ORCs) are an effective way to produce electricity from low-grade heat sources, which cannot be effectively obtained using conventional high-temperature Rankine cycles. Due to the lack of available information regarding the real Organic Rankine Cycle units on industrial level, off-design simulation under diversified operating conditions plays a significant role for both the system performance prediction and control strategy design. This paper summarizes the theoretical basis, modeling approaches and tools for ORC off-design simulations. Firstly, a review was conducted on the individual state-of-the-art convective heat transfer correlations and void fraction models. Secondly, different kinds of modeling approaches and simulation tools were proposed, highlighting their relevant characteristics, and were categorized for their specific applications. Moreover, an in-depth analysis of technical challenges related to various applications and focusing on the model accuracy and complexity, computational efficiency, as well as the model compatibility were extensively described and discussed. Finally, the current research trends in this field and the development for further investigations were presented.展开更多
文摘Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control(CMPC)strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.
基金financially supported by the National Key Basic Research Program of China 973 Program(Grant No.2014CB249201)
文摘Organic Rankine Cycles(ORCs) are an effective way to produce electricity from low-grade heat sources, which cannot be effectively obtained using conventional high-temperature Rankine cycles. Due to the lack of available information regarding the real Organic Rankine Cycle units on industrial level, off-design simulation under diversified operating conditions plays a significant role for both the system performance prediction and control strategy design. This paper summarizes the theoretical basis, modeling approaches and tools for ORC off-design simulations. Firstly, a review was conducted on the individual state-of-the-art convective heat transfer correlations and void fraction models. Secondly, different kinds of modeling approaches and simulation tools were proposed, highlighting their relevant characteristics, and were categorized for their specific applications. Moreover, an in-depth analysis of technical challenges related to various applications and focusing on the model accuracy and complexity, computational efficiency, as well as the model compatibility were extensively described and discussed. Finally, the current research trends in this field and the development for further investigations were presented.
