Niket S. Kaisare

Hierarchical multiscale model-based design of experiments, catalysts, and reactors for fuel processing

In this paper a hierarchical multiscale simulation framework is outlined and experimental data injection into this framework is discussed. Specifically, we discuss multiscale model-based design of experiments to optimize the chemical information content of a detailed reaction mechanism in order to improve the fidelity and accuracy of reaction models. Extension of this framework to product (catalyst) design is briefly touched upon. Furthermore, we illustrate the use of such detailed and reduced kinetic models in reactor optimization as an example toward more conventional process design. It is proposed that hierarchical multiscale modeling offers a systematic framework for identification of the important scale(s) and model(s) where one should focus research efforts on. The ammonia decomposition on ruthenium to produce hydrogen and the water–gas shift reactions on platinum for converting syngas to hydrogen serve as illustrative fuel processing examples of various topics. The former is used to illustrate hierarchical multiscale model development and model-based parameter estimation as well as product engineering. The latter is employed to demonstrate model reduction and process optimization. Finally, opportunities for process design and control in portable microchemical devices (lab-on-a chip) for power generation are discussed.

Optimal control of a fed-batch bioreactor using simulation-based approximate dynamic programming

In this brief, we extend the simulation-based approximate dynamic programming (ADP) method to optimal feedback control of fed-batch reactors. We consider a free-end problem, wherein the batch time is considered in finding the optimal feeding strategy in addition to the final time productivity. In ADP, the optimal solution is parameterized in the form of profit-to-go function. The original definition of profit-to-go is modified to include the decision of batch termination. Simulations from heuristic feeding policies generate the initial profit-to-go versus state data. An artificial neural network then approximates profit-to-go as a function of process state. Iterations of the Bellman equation are used to improve the profit-to-go function approximator. The profit-to-go function approximator thus obtained, is then implemented in an online controller. This method is applied to cloned invertase expression in Saccharomyces cerevisiae in a fed-batch bioreactor.

Approximate dynamic programming based control of hyperbolic PDE systems using reduced-order models from method of characteristics

Abstract Approximate dynamic programming (ADP) is a model based control technique suitable for nonlinear systems. Application of ADP to distributed parameter systems (DPS) which are described by partial differential equations is a computationally intensive task. This problem is addressed in literature by the use of reduced order models which capture the essential dynamics of the system. Order reduction of DPS described by hyperbolic PDEs is a difficult task as such systems exhibit modes of nearly equal energy. The focus of this contribution is ADP based control of systems described by hyperbolic PDEs using reduced order models. Method of characteristics (MOC) is used to obtain reduced order models. This reduced order model is then used in ADP based control for solving the set-point tracking problem. Two case studies involving single and double characteristics are studied. Open loop simulations demonstrate the effectiveness of MOC in reducing the order and the closed loop simulations with ADP based controller indicate the advantage of using these reduced order models.

Ignition strategies for fuel mixtures in catalytic microburners

Ignition of methane–air and propane–air mixtures over platinum catalyst in a parallel-plate microburner is studied numerically and a comparison of their ignition characteristics is presented. The ignition behaviour of the two fuels is compared for the case of heated feed and the strategy of using propane–methane mixed fuel is analysed. We show that adding small quantities of propane reduces the ignition temperature of lean methane–air mixture. Transient response of the mixed methane–propane fuel reveals sequential ignition of propane followed by methane. Sensitivity analysis on physical properties of methane and propane shows that the higher apparent activation energy of methane combustion accounts for most of the observed differences in their ignition behaviour. Ignition by resistive preheating, specifically the effect of locally preheating initial section of the burner is investigated. The amount of electric power required for ignition decreases with decrease in the electrical preheating length. This reduction in ignition power is especially significant for low conductivity walls, compared to highly conducting walls. Finally, the gap size of the channel has a relatively small effect on ignition in catalytic microburners.

The role of homogeneous chemistry during Ignition of propane combustion in Pt-catalyzed microburners

The aim of this work is to numerically investigate the ignition behavior of homogeneous-heterogeneous (HH) combustion of propane in a Pt-catalyzed microburner to delineate the role of homogeneous chemistry during cold-start ignition. A two dimensional model with one-step homogeneous and catalytic mechanisms in a parallel-plate microburner is considered. The ignition characteristics (ignition temperature and ignition time) are explored for catalytic microreactor with and without homogeneous chemistry. We show that the catalytic reaction lights-off first, followed by the homogeneous reaction. Consequently, the homogeneous chemistry does not affect the ignition behavior of the catalytic microburner. The effect of inlet velocity, wall thermal conductivity and gap size on ignition characteristics is explored. The ignition characteristics are not affected by homogeneous chemistry even at larger gap sizes, despite the fact that the homogeneous contribution at steady state increases with increasing gap size of the microburner.

Approximate Dynamic Programming based control for Water Gas Shift reactor

Abstract The Water Gas Shift (WGS) reactor is an important component in a fuel processing system. It is necessary to maintain low CO levels during steady state and transient operation of fuel processing system when used onboard in a vehicle. The WGS reactor plays an important role in this regard. The focus of this paper is a model based controller for the regulation of CO level in the presence of the possible disturbances from upstream temperature and flow rate. The use of model based controllers such as nonlinear Model Predictive Control (nMPC) suffers from large computational load. On the other hand Approximate Dynamic Programming (ADP) based controller will result in better performance with lower computational load. In this paper, a non-adiabatic WGS reactor modeled by hyperbolic partial differential equations is considered. The performance of the ADP is illustrated through numerical simulations.

Control and Optimization Challenges in Liquid-Loaded Shale Gas Wells

Abstract Shale gas reservoirs are classified as unconventional reservoirs. Their key features include low permeability, rapid decline in production rate, and liquid loading at the well-bottom. An industrial perspective towards automation in Shale gas is provided in this paper. Specifically, the challenges and opportunities in controlling the liquid loading problem and optimizing the production from shale gas wells are discussed. Automation systems and control hierarchy are discussed and parallels with the more familiar Process Industries are highlighted. The key components of reservoir modeling, well-bore modeling, feed-back control, model parameter update, multi-well optimization, and production management are presented. An example of periodic shut-in operation is used to underline the various concepts discussed in this paper.

Thermoacoustic Instabilities in a Ducted Premixed Flame: Reduced-Order Models and Control

The problem of combustion instabilities arising from the thermoacoustic interactions in a ducted premixed flame model is considered. Contrary to the conventionally used low-order models to describe such systems, a high dimensional model governed by a set of the acoustic equations coupled to the equations for flame dynamics is employed. The flame front is discretized into finite flame elements to assign internal degrees of freedom to the front and track its evolution. Model reduction schemes, namely proper orthogonal decomposition (POD) and balanced truncation, are used to reduce the size of this model. Compared to POD modes, balanced modes show superior input–output characteristics, in agreement with the full model. POD modes on the other hand capture the transient growth in the model while the balanced modes do not. Performance of POD modes is highly dependent on the snapshots used for their computation. A linear quadratic Gaussian (LQG) framework using the reduced-order model based on 16 balanced modes is formulated to control thermoacoustic instabilities in the linear model. The controllers thus obtained are then used on the nonlinear model. They successfully curtail limit cycle oscillations in the nonlinear plant and also avoid subcritical transition to instability.

Dynamic plunger lift model for deliquification of shale gas wells

Abstract This paper presents first principles model for operation of a plunger lift system in natural gas wells. The model consists of pressure and flow dynamics of fluids in annulus and the central tubing sections of the well, and dynamics of plunger fall and rise in the tubing. System dynamics switch on shutting or opening the production valve, and with autonomous events related to plunger motion. A nonlinear hybrid model is realized with nine states, switching though six stages (modes) of operation. This is the first instance of modeling complex system of plunger lift using a standard hybrid system model (HSM) framework. The resulting model is used to present insight into plunger lift operation, including an efficient simulation of multiple plunger cycles and analysis of effect of uncertainties on the well behavior.

Online optimization for a plunger lift process in shale gas wells

Abstract This paper presents a method for efficient optimization of a plunger lift process in shale gas wells. Plunger lift is a cyclic process consisting binary decision as well as continuous and discrete state variables. The time-series data comprising of surface measurements are converted into cycle-wise process-relevant performance outputs, while the binary manipulated variable is transformed into continuous threshold values. These transformed variables are used to develop a reduced order cycle-to-cycle model and corresponding receding horizon optimization problem that maximizes daily production while meeting operational constraints. The efficacy of the proposed algorithm is demonstrated on a simulated plunger lift process.