Optimization of the ammonia coverage ratio references in diesel engine two-can selective catalytic reduction systems via nonlinear model predictive control

2014 ◽  
Vol 228 (12) ◽  
pp. 1452-1467 ◽  
Author(s):  
Hui Zhang ◽  
Junmin Wang ◽  
Yue-Yun Wang
Keyword(s):  
Diesel Engine ◽  
Model Predictive Control ◽  
Selective Catalytic Reduction ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Catalytic Reduction ◽  
Nonlinear Model Predictive Control ◽  
Coverage Ratio

Diesel Engine Selective Catalytic Reduction (SCR) Ammonia Surface Coverage Control Using a Computationally-Efficient Model Predictive Control Assisted Method

2009 ◽  
Author(s):  
Ming Feng Hsieh ◽  
Junmin Wang
Keyword(s):  
Diesel Engine ◽  
Model Predictive Control ◽  
Selective Catalytic Reduction ◽  
Predictive Control ◽  
Surface Coverage ◽  
Catalytic Reduction ◽  
Computationally Efficient ◽  
Coverage Ratio ◽  
Control Objective ◽  
Control Authority

This paper presents a diesel engine selective catalytic reduction (SCR) control design based on a novel model predictive control (MPC)-assisted approach, which utilizes the advantages of MPC while keeping the computation demand under an acceptable level. The SCR control problem is featured by the challenges of time delay, significant time-varying characteristics, and limited control authority. Based on the understanding of the SCR reactions, the NH3 surface coverage ratio was selected as the control objective. The proposed MPC-assisted method was compared with conventional controllers such as PID and linear MPC (LMPC). Simulation results exhibited that the MPC-assisted approach can achieve a SCR ammonia surface coverage ratio control with much smaller root mean square error compared to these of other controllers while maintaining a manageable computational demand, and in turn better control of tailpipe NOx and ammonia emissions.

Nonlinear Model Predictive Control Strategies for a Parallel Evaporator Diesel Engine Waste Heat Recovery System

2016 ◽  
Author(s):  
Adamu Yebi ◽  
Bin Xu ◽  
Xiaobing Liu ◽  
John Shutty ◽  
Paul Anschel ◽  
...  
Keyword(s):  
Diesel Engine ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Pid Control ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Waste Heat ◽  
Nonlinear Model Predictive Control ◽  
Working Fluid

This paper discusses the control challenges of a parallel evaporator organic Rankine cycle (ORC) waste heat recovery (WHR) system for a diesel engine. A nonlinear model predictive control (NMPC) is proposed to regulate the mixed working fluid outlet temperature of both evaporators, ensuring efficient and safe ORC system operation. The NMPC is designed using a reduced order control model of the moving boundary heat exchanger system. In the NMPC formulation, the temperature difference between evaporator outlets is penalized so that the mixed temperature can be controlled smoothly without exceeding maximum or minimum working fluid temperature limits in either evaporator. The NMPC performance is demonstrated in simulation over an experimentally validated, high fidelity, physics based ORC plant model. NMPC performance is further validated through comparison with a classical PID control for selected high load and low load engine operating conditions. Compared to PID control, NMPC provides significantly improved performance in terms of control response time, overshoot, and temperature regulation.

Two-Level Nonlinear Model Predictive Control for Lean NOx Trap Regenerations

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Ming-Feng Hsieh ◽  
Junmin Wang ◽  
Marcello Canova
Keyword(s):  
Diesel Engine ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nox Reduction ◽  
Nonlinear Model Predictive Control ◽  
Lean Nox Trap ◽  
Nox Trap ◽  
Profile Control ◽  
Lean Nox

This paper describes a two-level nonlinear model predictive control (NMPC) scheme for diesel engine lean NOx trap (LNT) regeneration control. Based on the physical insights into the LNT operational characteristics, a two-level NMPC architecture with the higher-level for the regeneration timing control and the lower-level for the regeneration air to fuel ratio profile control is proposed. A physically based and experimentally validated nonlinear LNT dynamic model is employed to construct the NMPC control algorithms. The control objective is to minimize the fuel penalty induced by LNT regenerations while keeping the tailpipe NOx emissions below the regulations. Based on the physical insights into the LNT system dynamics, different choices of cost function were examined in terms of the impacts on fuel penalty and tailpipe NOx slip amount. The designed control system was evaluated on an experimentally validated vehicle simulator, cX-Emissions, with a 1.9 l diesel engine model through the FTP75 driving cycle. Compared with a conventional LNT control strategy, 31.9% of regeneration fuel penalty reduction was observed during a single regeneration. For the entire cold-start FTP75 test cycle, a 28.1% of tailpipe NOx reduction and 40.9% of fuel penalty reduction were achieved.

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