Combustion cyclic variability in an internal combustion engine leads to cyclic variations in the engine torque output and emissions. Combustion cyclic variability is often characterized by coefficient of variation of ...
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Combustion cyclic variability in an internal combustion engine leads to cyclic variations in the engine torque output and emissions. Combustion cyclic variability is often characterized by coefficient of variation of indicated mean effective pressure (COV IMEP ) that is used as an indicator of combustion stability. These cyclic variations are inevitable and cannot be completely eliminated but can be controlled to allow stable engine operation. This work focuses on control oriented modeling of COV IMEP to limit engine cyclic variations in low temperature combustion (LTC) modes. COV IMEP is generally stochastic in nature; thus, a data-driven approach is used to develop a predictivemodel of COV IMEP for Homogeneous Charge Compression Ignition (HCCI) and Reactivity controlled Compression Ignition (RRCI) modes. This work presents the development of a cycle-by-cycle modelpredictivecontroller for a 2.0 liter multi-mode LTC engine. Physics-based control-oriented models for combustion phasing (CA50) and IMEP are augmented with the new data-driven COV IMEP model to limit the cyclic variations below 3% for HCCI and RCCI modes. These models are then used to design closed-loop non-linear model predictive controllers to control CA50 and IMEP while constraining COV IMEP to ensure stable engine operation for varying load conditions.
This paper surveys how the three central pillars of process control – PID control, conventional advanced control, and modelpredictivecontrol – have been used and how they have contributed to production activity fr...
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This paper surveys how the three central pillars of process control – PID control, conventional advanced control, and modelpredictivecontrol – have been used and how they have contributed to production activity from the viewpoint of the process control section in the Japanese chemical industry. In addition to introducing practical methods and their application results, the authors point out challenging problems, which include the development of a general model-based control technique to enhance batch process control.
Abstract Chemical processes are usually operated under feedback control of critical operating conditions to maintain process safety, product quality and economic performance. Increasingly, linearmodelpredictive is a...
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Abstract Chemical processes are usually operated under feedback control of critical operating conditions to maintain process safety, product quality and economic performance. Increasingly, linearmodelpredictive is applied to these coupled multivariable control problems and in some cases the reference values are adapted infrequently by an optimization based as a rigorous nonlinear stationary plant model (RTO). However, in between these optimizations that plant may operate suboptimally due to the presence of disturbances. In recent years, it has been proposed to use nonlinearmodel-based optimization of the available degrees of freedom of a process over a finite moving horizon to integrate set-point optimization and feedback control (for a survey see Engell (2007)). In this paper we demonstrate the feasibility and the potential of the optimizing nonlinearcontrol for a challenging example, a reactive distillation column. The performance of the controller is an economics-driven response since the profit function represents the main part of the objective criterion while the quality specifications as well as the physical limitations of the process were incorporated as optimization constraints. A performance comparison between the tracking controller and the optimizing nonlinearcontroller is also shown.
control of a bio-reactor is a complex task due to inherent non-linearities and unavailability of measurements of the quality variables at regular sampling intervals. In this work, it proposed to identify Wiener-Hammer...
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control of a bio-reactor is a complex task due to inherent non-linearities and unavailability of measurements of the quality variables at regular sampling intervals. In this work, it proposed to identify Wiener-Hammerstein type fast-rate time series models for the quality variables directly from the irregularly sampled multi-rate input-output data. The identified models are further used to develop a multi-rate nonlinearpredictivecontroller. The efficacy of the proposed modelling and control scheme is demonstrated by conducting simulation studies on a continuous fermenter system that exhibits input multiplicity and gain reversal in the desired operating region.
This paper describes the application of nonlinearmodelpredictivecontrol (NMPC) to the thermal control of a batch or semi-batch chemical reactor fitted with an alternative heating/cooling system. The strategy of the...
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This paper describes the application of nonlinearmodelpredictivecontrol (NMPC) to the thermal control of a batch or semi-batch chemical reactor fitted with an alternative heating/cooling system. The strategy of the nonlinearcontrol system is based on an open-loop constrained optimisation problem which is solved repeatedly on-line by a sequential optimisation and solution strategy on a PC for a typical temperature sampling rate. The feasibility of such a controller is examined on simulations and experiments on a 16 litres pilot plant. The presented results are satisfactory and are promising for an industrial use of such a technique.
In the modern era, managing optimal real-time control of microgrids during the operation phase has been a significant challenge, requiring careful consideration of both technical and economic factors. This paper intro...
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