Data Driven and Machine Learning Based Modeling and Predictive Control of Combustion at Reactivity Controlled Compression Ignition Engines

Author

Behrouz Khoshbakht Irdmousa, Michigan Technological UniversityFollow

Date of Award

2024

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Mechanical Engineering-Engineering Mechanics (PhD)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Jeffrey Naber

Advisor 2

Mahdi Shahbakhti

Committee Member 1

Jeremy Worm

Committee Member 2

David Wanless

Abstract

Reactivity Controlled Compression Ignition (RCCI) engines operates has capacity to provide higher thermal efficiency, lower particular matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) operation. Achieving these benefits is difficult since real-time optimal control of RCCI engines is challenging during transient operation. To overcome these challenges, data-driven machine learning based control-oriented models are developed in this study. These models are developed based on Linear Parameter-Varying (LPV) modeling approach and input-output based Kernelized Canonical Correlation Analysis (KCCA) approach. The developed dynamic models are used to predict combustion timing (CA50), indicated mean effective pressure (IMEP), maximum pressure rise rate (MPRR), reactivity and stratification metrics as functions of fuel quantity (FQ), start of injection (SOI) timing and premixed ratio (PR) as the RCCI engine's control variables. The identified model is then used for the design of a model predictive controller (MPC) to control crank angle for 50\% fuel burnt (CA50) for varying engine conditions on the actual RCCI engine. This study also established constrained multi-input multi-output (MIMO) model predictive controller (MPC) to track desired crank angle for 50\% fuel burnt and IMEP at various engine conditions. This research has demonstrated and implemented two fast and reliable method to model highly nonlinear RCCI combustion engine and develop control-oriented models for RCCI combustion

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Irdmousa, Behrouz Khoshbakht, "Data Driven and Machine Learning Based Modeling and Predictive Control of Combustion at Reactivity Controlled Compression Ignition Engines", Open Access Dissertation, Michigan Technological University, 2024.

https://digitalcommons.mtu.edu/etdr/1703