Microphotonic Harsh Environment Sensors for Clean Fuel and Power Generation

Track IV: Materials for Energy ApplicationsIncludes audio file (19 min.)Low cost, reliable, in-situ sensors are highly desired for advanced process control and lifecycle management in various power and fuel systems. Many energy generation processes involve harsh conditions throughout the operation that requires monitoring to assist in attaining and maintaining the goals of high efficiency and high environmental performance. General measurements of interest include temperature, strain, pressure, gas compositions, and trace contaminants/pollutants. Unfortunately, most existing sensors are incapable of operating directly in the harsh environment of typical power and fuel systems involving high temperature and high pressure with presence of particulates and corrosive atmosphere. Funded by DoE/NETL, our group has been developing various novel microphotonic sensors for monitoring physical and chemical parameters under hostile conditions. The demonstrated sensors include the miniaturized inline fiber Fabry-Perot interferometer (FPI) fabricated by one-step femtosecond laser micromaching, the long period fiber grating (LPFG) fabricated by CO2 laser irradiations, the fiber inline core-cladding mode interferometers (CMMI), and the LPFG coupled CMMI sensors. These sensors can be directly used for the measurements of various physical parameters such as temperature, pressure and strain in a high temperature (tested up to 1100 degree C) harsh environment are presented. In addition, when coated with a thin layer of gas sensitive thin film (e.g., doped crystalline ceramic nanofilm), they can be used for measurements of various hot gases such as hydrogen, carbon dioxide, carbon monoxide, and hydrogen sulfide in high temperatures. With demonstrated advantages of small size, lightweight, immunity to electromagnetic interference, resistance to chemical corrosion, high sensitivity, remote operation capability, robustness and dependable performance in a hostile environment, these microphotonic sensor may find broad applications for process control and optimization in various fuel/power systems such as coal gasification, advanced engines, oil/gas extraction, fuel cell operation, coal/geothermal/wind/nuclear-based power generations, etc. We hope that this presentation will convey our interest in teaming up with UM researchers and industry partners to collectively explore future opportunities

Near Optimal Output-Feedback Control of Nonlinear Discrete-Time Systems in Nonstrict Feedback Form with Application to Engines

A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. A Lyapunov function proves the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight, and observer estimation. Separation principle and certainty equivalence principles are relaxed; persistency of excitation condition and linear in the unknown parameter assumption is not needed. The performance of this controller is evaluated on a spark ignition (SI) engine operating with high exhaust gas recirculation (EGR) levels and experimental results are demonstrated

Neural Network Control of Spark Ignition Engines with High EGR Levels

Research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% to 25% exhaust gas recirculation (EGR) in spark ignition (SI) engines [1]. However under high EGR levels the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance. A suite of neural network (NN)-based output feedback controllers with and without reinforcement learning is developed to control the SI engine at high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. For reinforcement learning, an additional NN is used as the critic. The stability analysis of the closed loop system is given and the boundedness of all signals is ensured without separation principle. Online training is used for the adaptive NN and no offline training phase is needed. Experimental results obtained by testing the controller on a research engine indicate an 80% drop of NOx from stoichiometric levels using 10% EGR. Moreover, unburned hydrocarbons drop by 25% due to NN control as compared to the uncontrolled scenario

Reinforcement Learning Based Dual-Control Methodology for Complex Nonlinear Discrete-Time Systems with Application to Spark Engine EGR Operation

A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown disturbances. For example, the exhaust gas recirculation (EGR) operation of a spark ignition (SI) engine is modeled by using such a complex nonlinear discrete-time system. A dual-controller approach is undertaken where primary adaptive critic NN controller is designed for the nonstrict feedback nonlinear discrete-time system whereas the secondary one for the affine nonlinear discrete-time system but the controllers together offer the desired performance. The primary adaptive critic NN controller includes an NN observer for estimating the states and output, an NN critic, and two action NNs for generating virtual control and actual control inputs for the nonstrict feedback nonlinear discrete-time system, whereas an additional critic NN and an action NN are included for the affine nonlinear discrete-time system by assuming the state availability. All NN weights adapt online towards minimization of a certain performance index, utilizing gradient-descent-based rule. Using Lyapunov theory, the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight estimates, and observer estimates are shown. The adaptive critic NN controller performance is evaluated on an SI engine operating with high EGR levels where the controller objective is to reduce cyclic dispersion in heat release while minimizing fuel intake. Simulation and experimental results indicate that engine out emissions drop significantly at 20% EGR due to reduction in dispersion in heat release thus verifying the dual-control approach

Reinforcement-Learning-Based Output-Feedback Control of Nonstrict Nonlinear Discrete-Time Systems with Application to Engine Emission Control

A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility function and control inputs. All NN weights adapt online toward minimization of a performance index, utilizing the gradient-descent-based rule, in contrast with iteration-based adaptive-critic schemes. Lyapunov functions are used to show the stability of the closed-loop tracking error, weights, and observer estimates. Separation and certainty equivalence principles, persistency of excitation condition, and linearity in the unknown parameter assumption are not needed. Experimental results on a spark ignition (SI) engine operating lean at an equivalence ratio of 0.75 show a significant (25%) reduction in cyclic dispersion in heat release with control, while the average fuel input changes by less than 1% compared with the uncontrolled case. Consequently, oxides of nitrogen (NOx) drop by 30%, and unburned hydrocarbons drop by 16% with control. Overall, NOx\u27s are reduced by over 80% compared with stoichiometric levels

Output Feedback Controller for Operation of Spark Ignition Engines at Lean Conditions Using Neural Networks

Spark ignition (SI) engines operating at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle bifurcation of heat release. Past literature suggests that operating an engine under such lean conditions can significantly reduce NO emissions by as much as 30% and improve fuel efficiency by as much as 5%-10%. At lean conditions, the heat release per engine cycle is not close to constant, as it is when these engines operate under stoichiometric conditions where the equivalence ratio is 1.0. A neural network controller employing output feedback has shown ability in simulation to reduce the nonlinear cyclic dispersion observed under lean operating conditions. This neural network (NN) output controller consists of three NNs: a) an NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. The uniform ultimate boundedness of all closed-loop signals is demonstrated by using the Lyapunov analysis without using the separation principle. Persistency of the excitation condition, the certainty equivalence principle, and the linearity in the unknown parameter assumptions are also relaxed. The controller is implemented for a research engine as a program running on an embeddable PC that communicates with the engine through a custom hardware interface, and the results are similar to those observed in simulation. Experimental results at an equivalence ratio of 0.77 show a drop in NO emissions by around 98% from stoichiometric levels with an improvement of fuel efficiency by 5%. A 30% drop in unburned hydrocarbons from uncontrolled case is observed at this equivalence ratio of 0.77. Similar performance was observed with the controller on a different engine

Implementation of Advanced Fuels and Combustion for Internal Combustion Engines

Track II: Transportation and BiofuelsIncludes audio file (19 min.)Advanced engine designs for transportation has shown significant reduction in engine-out emissions while simultaneously achieving gains in fuel efficiency by using Low Temperature Combustion (LTC) modes and feedback control of the combustion processes. The work of this group has considered the difficulties encountered in using these combustion modes through implementation of advanced control methodologies, novel sensor techniques as well as expanding usage of fuels such as bio-fuels and hydrogen. The methods used to obtain the lower combustion temperatures include lean mixtures and high levels of exhaust gas recirculation. For example, LTC modes such as Homogeneous Charge Compression Ignition (HCCI) and Partially Premixed Compression Ignition (PCCI) engines show real gains in reduced engine out emissions with improved efficiency. However, implementation of these advanced combustion modes presents combustion timing and stability issues due to stronger dependence of these advanced combustion modes on the physical and chemical properties of the fuel, inlet temperature, and inlet composition than traditional diffusion burning (“diesel” type) modes. Progress in these advanced combustion modes requires a “smart” engine capable of sensing heat release patterns and adjusting combustion system parameters. Hence collaborative work between several researchers at Missouri S&T are considering the required combustion analysis, nonlinear control, sensor development and fuel property issues surrounding the implementation of several LTC modes. Analysis methods currently considered are based on surface accelerations for use on both conventional and premixed auto-ignited combustion types that can robustly indicate combustion characteristics. Surface mount accelerometers are being used to indicate combustion characteristics needed for closed loop engine control but which have minimal structural influence. Acceleration frequency bands are being identified where the structural characteristics has the most influence (i.e. structure resonant modes), thereby allowing indication of other surface acceleration frequency bands which are minimally affected by the structure and more indicative of the combustion behavior. Active control necessitates an advanced control strategy such as adaptive neural networks which we have shown can function satisfactorily even when the dynamics of the engine combustion process are unknown. A near optimal nonlinear adaptive controller using Approximate Dynamic Programming (ADP), based on a phenomenological LTC engine model is being developed. The conceived controller would reduce cyclic variability in start-of-combustion, limit pressure rise rates and control to maximize efficiency through control of heat release pattern phasing. With advanced control algorithms, low-cost sensor technologies need to be developed before robust control of auto-ignited combustion can be achieved on a production scale. Interferometer based sensors packaged in small fiber optics are being developed for the high temperature and pressure combustion chamber environment with response times on the order of microseconds. Finally, advancing the application of advanced LTC modes to enable the use of bio-fuels or hydrogen has become increasingly important for energy security. Consequently, the distinct characteristics of hydrogen combustion in engines are being investigated using advanced simulation techniques to examine more efficient and cleaner operating strategies (e.g., dual-fuel operation)

Neural Network Controller Development and Implementation for Spark Ignition Engines with High EGR Levels

Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The stability analysis of the closed-loop system is given and the boundedness of the control input is demonstrated by relaxing separation principle, persistency of excitation condition, certainty equivalence principle, and linear in the unknown parameter assumptions. Online training is used for the adaptive NN and no offline training phase is needed. This online learning feature and model-free approach is used to demonstrate the applicability of the controller on a different engine with minimal effort. Simulation results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller when implemented on an engine model that has been validated experimentally. For a single cylinder research engine fitted with a modern four-valve head (Ricardo engine), experimental results at 15% EGR indicate that cyclic dispersion was reduced 33% by the controller, an improvement of fuel efficiency by 2%, and a 90% drop in NOx from stoichiometric operation without EGR was observed. Moreover, unburned hydrocarbons (uHC) drop by 6% due to NN control as compared to the uncontrolled scenario due to the drop in cyclic dispersion. Similar performance was observed with the controller on a different engine

Reinforcement Learning Based Output-Feedback Control of Nonlinear Nonstrict Feedback Discrete-Time Systems with Application to Engines

A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. A Lyapunov function proves the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight, and observer estimation. Separation principle and certainty equivalence principles are relaxed; persistency of excitation condition and linear in the unknown parameter assumption is not needed. The performance of this adaptive critic NN controller is evaluated through simulation with the Daw engine model in lean mode. The objective is to reduce the cyclic dispersion in heat release by using the controller

Neural Network-Based Output Feedback Controller for Lean Operation of Spark Ignition Engines

Spark ignition (SI) engines running at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle dispersion of heat release even though such operation can significantly reduce NOx emissions and improve fuel efficiency by as much as 5-10%. A suite of neural network (NN) controller without and with reinforcement learning employing output feedback has shown ability to reduce the nonlinear cyclic dispersion observed under lean operating conditions. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. For reinforcement learning, an additional NN is used as the critic. The uniform ultimate boundedness of all closed-loop signals is demonstrated by using Lyapunov analysis without using the separation principle. Experimental results on a research engine at an equivalence ratio of 0.77 show a drop in NOx emissions by around 98% from stoichiometric levels. A 30% drop in unburned hydrocarbons from uncontrolled case is observed at this equivalence ratio