Dynamic Modeling, Parameter Estimation, and Data Reconciliation of a Supercritical Pulverized Coal-Fired Boiler

Abstract

A high-fidelity, spatially distributed dynamic model of a supercritical pulverized coal-fired boiler was developed for calculating transient thermal profiles of the boiler tubes, which are often not measured or unmeasurable in industrial settings due to the harsh operating conditions. Rigorous models of the subcritical and supercritical steam properties, which can be highly nonlinear, were used to capture the transition between two-phase and one-phase flow, respectively, as supercritical boilers frequently transition through the critical point during load-following operation. The transient boiler model was also used for data reconciliation and parameter estimation for the purpose of model validation against industrial data. An approach was developed to reduce the size of the large-scale reconciliation problem by focusing on the implementation of bias terms for the reconciled variables, which can be estimated in place of the reconciled variables themselves. In addition, a functional approximation of the reconciliation problem was defined so that it can be more simply optimized. Combining this functional approximation optimization with reformulation of the reconciliation problem considering measurement biases can significantly improve the tractability of many such large-scale problems. The validated boiler model was then used to study transient responses under load-following operation with specific attention on the calculation of unmeasurable variables.

Hot Corrosion Monitoring in Boilers Using a Nonlinear Estimator and Electrochemical Noise-Based Corrosion Sensors

“…The temperature at the probe location is 550 °C, whereas the exact time-varying O 2 and SO 2 concentrations are unknown. However, SO 2 and O 2 concentrations at IPP are estimated for some specific time instants from the work of Hedrick et al 7 Thus, using a single value for the inputs metal temperature and O 2 and SO 2 concentrations, the corrosion model for both alloys calculates the corrosion depth. The scaled model results are compared with the scaled IPP data in Figure 5.…”

“…Temperature. By using the data for O 2 concentration from Seltzer et al 52 and metal temperature from Hedrick et al, 7 feedforward neural network models with a single hidden node are found to yield a mean percentage error of 5.3% for temperature and a mean absolute error of 0.01 atm for O 2 concentration. In this work, the activation function used for the output layer φ o (•) is a linear transfer function.…”

“…Therefore, nonrenewable sources like coal are still expected to be prevalent and be used to ensure the stability of the electric grid in the foreseeable future especially for countries where coal is abundantly available and even subsidized by the government. , The existing coal-fired power plants are generally designed to operate under base-loaded conditions. Under load-following conditions to meet the electricity demand, power plants are subjected to frequent startups and shutdowns and ramping up and down of production. − The rapid fluctuations in operating conditions, especially process temperature, can cause significant damage to the health of the critical components of power plants, causing unplanned outages . While several approaches have been recommended to minimize damage, , it is difficult to avoid excursion in temperature under aggressive cycling .…”

Design and Multiobjective Dynamic Optimization of Superheaters for Load-Following Operation in Pulverized Coal Power Plants

“…Although the flue gas flow direction and the overall steam flow direction are perpendicular to one another, the energy balances are still one-dimensional for each fluid. 36 The energy conservation for the flue gas side is given by eq 1…”

“…Those considerations lead to the development of a 2D partial differential equation (PDE) first-principles model. Although the flue gas flow direction and the overall steam flow direction are perpendicular to one another, the energy balances are still one-dimensional for each fluid . The energy conservation for the flue gas side is given by eq ∂ ( ρ Ĥ ) ∂ t = prefix− ∂ ( ρ v Ĥ ) ∂ z − q wall In eq , ρ, v , and Ĥ denote the density, velocity, and specific enthalpy of the flue gas, respectively, and q wall denotes the heat loss from the flue gas to the shell-side boundary of the tube wall.…”