Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant

There is increasing need for tighter controls of coal-fired plants due to more stringent regulations and addition of more renewable sources in the electricity grid. Achieving this will require better process knowledge which can be facilitated through the use of plant models. Drum-boilers, a key component of coal-fired subcritical power plants, have complicated characteristics and require highly complex routines for the dynamic characteristics to be accurately modelled. Development of such routines is laborious and due to computational requirements they are often unfit for control purposes. On the other hand, simpler lumped and semi empirical models may not represent the process well. As a result, data-driven approach based on neural networks is chosen in this study. Models derived with this approach incorporate all the complex underlying physics and performs very well so long as it is used within the range of conditions on which it was developed. The model can be used for studying plant dynamics and design of controllers. Dynamic model of the drum-boiler was developed in this study using NARX neural networks. The model predictions showed good agreement with actual outputs of the drum-boiler (drum pressure and water level)

NARX models for simulation of the start-up operation of a single-shaft gas turbine

In this study, nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine (GT) are developed and validated. The GT is a power plant gas turbine (General Electric PG 9351FA) located in Italy. The data used for model development are three time series data sets of two different maneuvers taken experimentally during the start-up procedure. The resulting NARX models are applied to three other experimental data sets and comparisons are made among four significant outputs of the models and the corresponding measured data. The results show that NARX models are capable of satisfactory prediction of the GT behavior and can capture system dynamics during start-up operation

A Data-Based Approach for Modeling and Analysis of Vehicle Collision by LPV-ARMAX Models

Vehicle crash test is considered to be the most direct and common approach to assess the vehicle crashworthiness. However, it suffers from the drawbacks of high experiment cost and huge time consumption. Therefore, the establishment of a mathematical model of vehicle crash which can simplify the analysis process is significantly attractive. In this paper, we present the application of LPV-ARMAX model to simulate the car-to-pole collision with different initial impact velocities. The parameters of the LPV-ARMAX are assumed to have dependence on the initial impact velocities. Instead of establishing a set of LTI models for vehicle crashes with various impact velocities, the LPV-ARMAX model is comparatively simple and applicable to predict the responses of new collision situations different from the ones used for identification. Finally, the comparison between the predicted response and the real test data is conducted, which shows the high fidelity of the LPV-ARMAX model

Intelligent modeling of double link flexible robotic manipulator using artificial neural network

The paper investigates the application of the Artificial Neural Network (ANN) in modeling of double-link flexible robotic manipulator (DLFRM). The system was categorized under multi-input multi-output. In this research, the dynamic models of DLFRM were separated into single-input single-output in the modeling stage. Thus, the characteristics of DLFRM were defined separately in each model and the coupling effect was assumed to be minimized. There are four discrete SISO model of double link flexible manipulator were developed from torque input to the hub angle and from torque input to the end point accelerations of each link. An experimental work was established to collect the input-output data pairs and used in developing the system model. Since the system is highly nonlinear, NARX model was chosen as the model structure because of its simplicity. The nonlinear characteristic of the system was estimated using the ANN whereby multi-layer perceptron (MLP) and ELMAN neural network (ENN) structure were utilized. The implementation of the ANN and its’ effectiveness in developing the model of DLFRM was emphasized. The performance of the MLP was compared to ENN based on the validation of the mean-squared error (MSE) and correlation tests of the developed models. The results indicated that the identification of the DLFRM system using the MLP outperformed the ENN with lower mean squared prediction error and unbiased results for all the models. Thus, the MLP provides a good approximation of the DLFRM dynamic model compared to the ENN

Modelling and intelligent control of double-link flexible robotic manipulator

The use of robotic manipulator with multi-link structure has a great influence in most of the current industries. However, controlling the motion of multi-link manipulator has become a challenging task especially when the flexible structure is used. Currently, the system utilizes the complex mathematics to solve desired hub angle with the coupling effect and vibration in the system. Thus, this research aims to develop a dynamic system and controller for double-link flexible robotics manipulator (DLFRM) with the improvement on hub angle position and vibration suppression. A laboratory sized DLFRM moving in horizontal direction is developed and fabricated to represent the actual dynamics of the system. The research utilized neural network as the model estimation. Results indicated that the identification of the DLFRM system using multi-layer perceptron (MLP) outperformed the Elman neural network (ENN). In the controllers’ development, this research focuses on two main parts namely fixed controller and adaptive controller. In fixed controller, the metaheuristic algorithms known as Particle Swarm Optimization (PSO) and Artificial Bees Colony (ABC) were utilized to find optimum value of PID controller parameter to track the desired hub angle and supress the vibration based on the identified models obtained earlier. For the adaptive controller, self-tuning using iterative learning algorithm (ILA) was implemented to adapt the controller parameters to meet the desired performances when there were changes to the system. It was observed that self-tuning using ILA can track the desired hub angle and supress the vibration even when payload was added to the end effector of the system. In contrast, the fixed controller degraded when added payload exceeds 20 g. The performance of these control schemes was analysed separately via real-time PC-based control. The behaviour of the system response was observed in terms of trajectory tracking and vibration suppression. As a conclusion, it was found that the percentage of improvement achieved experimentally by the self-tuning controller over the fixed controller (PID-PSO) for settling time are 3.3 % and 3.28 % of each link respectively. The steady state errors of links 1 and 2 are improved by 91.9 % and 66.7 % respectively. Meanwhile, the vibration suppression for links 1 and 2 are improved by 76.7 % and 67.8 % respectively

Artificial Neural Networks, Non Linear Auto Regression Networks (NARX) and Causal Loop Diagram Approaches for Modelling Bridge Infrastructure Conditions

The quality of highway bridge infrastructure in United States is of major concern. One in every four bridges in the US is deficient. This research applied Artificial Intelligence, Systems Dynamics and linear modeling techniques to investigate the causes and effects of bridge deterioration and to forecast bridge infrastructure condition and improvement costs. The main contribution of the research is the development and demonstration of these methods within the context of highway bridges. These methods provide bridge designers and policy makers new tools for maintaining, improving, and delivering high quality bridge infrastructure. To start with, a comprehensive review of the current state of bridge deficiency in US was conducted. Through extensive data mining of the National Bridge Inventory (NBI), the causes and trends in bridge deficiency were identified. This exercise addressed questions such as: What is the current extent of bridge deficiency? Is deficiency getting better or worse? What are the biggest problems causing deficiencies? It was observed that though the general condition of bridges is improving, additional work needs to be done in fixing bridge deficiency and bridge functionally obsolescence in particular. Subsequent to the review of bridge deficiency, four distinct but related modeling studies were conducted. These phases are: 1) Capacity Obsolescence/Sustainability assessment, 2) Causal Loop Diagram (CLD) and linear modeling for bridge improvement costs, 3) Artificial Neural Network (ANN) model for bridge condition ratings and bridge variable effects, 4) Non-linear auto regression (NARX) model for bridge inventory condition prediction. In the first phase, a conceptual model was developed to minimize capacity obsolescence, one face of functional obsolescence. A framework was developed to minimize bridge capacity obsolescence while optimizing the use of embodied energy over the service life of bridges. The research demonstrated how design phase consideration of bridge obsolescence can contribute to sustainability of bridge infrastructure. As a novel approach for studying bridge improvement costs, the second phase used a Causal Loop Diagram (CLD), a tool used in the field of System Dynamics. Using a CLD, the causes and effects for bridge deterioration were qualitatively described. A segment of the qualitative relationships described through the CLD were then analyzed quantitatively for the South Carolina bridge inventory. The quantitative model was based on linear modeling and was developed and validated using NBI data. The model was then applied to estimate future bridge inventory sufficiency ratings and improvement costs under possible funding scenarios. For effective mitigation of bridge deficiency, it is important to identify the effects of different variables on bridge conditions and forecast bridge condition. In the third phase of modeling, Artificial Neural Networks (ANN) models were used to study the effects of bridge variables on bridge deck and superstructure condition ratings. The models considered prestressed concrete bridges in South Eastern United States. Simulations based on Full Factorial Design (FFD) were conducted using the developed ANN models. The simulations highlighted the effects of skew, span and age on bridge condition ratings. Given sufficient source data, the approach can be broadly applied to consider other bridge types and design variables. In the last phase, time based ANN learning algorithms were used to forecast bridge condition ratings and bridge improvement costs. Non Linear Auto Regression with Exogenous Inputs (NARX) model was developed using NBI data for South Carolina bridges over the last decade. The study estimated bridge condition ratings as a function of bridge geometry, age, structural, traffic attributes and bridge improvement spending. This doctoral research contributed to the development of multiple qualitative and mathematical models for forecasting bridge inventory condition and improvement costs by applying ANN, CLD, and linear regression techniques. While the conclusions of these studies are bound by the scope of the data and methodical constraints of the research, the methods can be more generally applied to aid in better bridge management policies and contribute to sustainable bridge infrastructure in United States

Study of power plant, carbon capture and transport network through dynamic modelling and simulation

The unfavourable role of CO₂ in stimulating climate change has generated concerns as CO₂ levels in the atmosphere continue to increase. As a result, it has been recommended that coal-fired power plants which are major CO₂ emitters should be operated with a carbon capture and storage (CCS) system to reduce CO₂ emission levels from the plant. Studies on CCS chain have been limited except a few high profile projects. Majority of previous studies focused on individual components of the CCS chain which are insufficient to understand how the components of the CCS chain interact dynamically during operation. In this thesis, model-based study of the CCS chain including coal-fired subcritical power plant, post-combustion CO₂ capture (PCC) and pipeline transport components is presented. The component models of the CCS chain are dynamic and were derived from first principles. A separate model involving only the drum-boiler of a typical coal-fired subcritical power plant was also developed using neural networks.The power plant model was validated at steady state conditions for different load levels (70-100%). Analysis with the power plant model show that load change by ramping cause less disturbance than step changes. Rate-based PCC model obtained from Lawal et al. (2010) was used in this thesis. The PCC model was subsequently simplified to reduce the CPU time requirement. The CPU time was reduced by about 60% after simplification and the predictions compared to the detailed model had less than 5% relative difference. The results show that the numerous non-linear algebraic equations and external property calls in the detailed model are the reason for the high CPU time requirement of the detailed PCC model. The pipeline model is distributed and includes elevation profile and heat transfer with the environment. The pipeline model was used to assess the planned Yorkshire and Humber CO₂ pipeline network.Analysis with the CCS chain model indicates that actual changes in CO₂ flowrate entering the pipeline transport system in response to small load changes (about 10%) is very small (<5%). It is therefore concluded that small changes in load will have minimal impact on the transport component of the CCS chain when the capture plant is PCC

Modeling Dynamic Systems for Multi-Step Prediction with Recurrent Neural Networks

This thesis investigates the applicability of Recurrent Neural Networks (RNNs) and Deep Learning methods for multi-step prediction of robotic systems. The unmodeled dynamics and simplifying assumptions in classic modeling methods result in models that yield rapidly diverging predictions when the model is used in an iterative fashion, i.e., for multi-step prediction. However, the effect of the unmodeled dynamics can be captured by collecting datasets of the system. Deep Learning provides a strong set of tools to extract patterns from data, however, large datasets are commonly required for the methods to work well. Collecting a large amount of data from a robotic system can be a cumbersome and expensive approach. In this work, Deep Learning methods, particularly RNNs, are studied and employed for the purpose of learning models of two aerial vehicles from experimental data. The feasibility of employing RNNs is first studied to learn a model of a quadrotor based on a simulated dataset, which yields a Multi Layer Fully Connected (MLFC) architecture. Models can be learned for multi-step prediction, recovering excellent predictions over 500 timesteps in the presence of simulated disturbances to the robot and noise on the measurements. To learn models from experimental data, the RNN state initialization problem is defined and formulated. It is shown that the RNN state initialization problem can be addressed by creating and training an initialization network jointly with the multi-step prediction network, and the combination can be used in a black-box modeling approach such that the model produces predictions which are immediately accurate. The RNN based black-box methods are trained on an experimental dataset gathered from a quadrotor and a publicly available helicopter dataset. The quadrotor dataset, which encompasses approximately 4 hours of flight data in various regimes, has been released and is now available publicly online. Finally, a hybrid network, which combines the proposed RNN based black-box models with a physics based quadrotor model into a single RNN-based modeling system is introduced. The proposed hybrid network solves many of the limitations of the existing state of the art in long-term prediction for robotics systems. Trained on the quadrotor dataset, the hybrid model provides accurate body angular rate and velocity predictions of the vehicle over almost 2 seconds which is suitable to be used in a variety of model-based controller applications

An integrated combined methodology for the outline gas turbines performance-based diagnostics and signal failure isolation.

The target of this research is the performance-based diagnostics of a gas turbine for the online automated early detection of components malfunctions with the presence of measurements malfunctions. The research proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high rate of success for single and multiple failures with the presence of measurement malfunctions – measurement noise. A combination of Kalman Filter, Artificial Neural Network, Neuro-Fuzzy Logic and Fuzzy Logic is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman Filter has in his strength the measurement failure treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile, the neuro-fuzzy logic the estimation precision, used for the quantification and the fuzzy logic the categorization flexibility, which are used to classify the components failure. All contributors are also a valid technique for online diagnostics, which is a key objective of the methodology. In the area of gas turbine diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively. This research investigates the key contribution of each component of the methodology and reaches a success rate for the component health estimation above 92.0% and a success rate for the failure type classification above 95.1%. The results are obtained with the first configuration, running with the reference random simulation of 203 points with different level of deterioration magnitude and different combinations of failures type. If a measurement noise 5 times higher than the nominal is considered, the component health estimation drop to a minimum of 70.1% (reference scheme 1) while the classification success rate remains above 88.9% (reference scheme 1). Moreover, the speed of the data processing – minimum 0.23 s / maximum 1.7 s per every single sample – proves the suitability of this methodology for online diagnostics. The methodology is extensively tested against components failure and measurement issues. The tests are repeated with constant simulations, random simulation and a deterioration schedule that is reproducing several months of engine operations.PhD in Aerospac