Internet based data logging and supervisory control of boiler drum level using LabVIEW

This work describes a framework of a Internet based data logging and supervisory control of boiler drum level system. The design and implementation of this process is done by the LabVIEW software. The data of the process variables (Temperature and Level) from the boiler system need to be logged in a database for further analysis and supervisory control. A LabVIEW based data logging and supervisory control program simulates the process and the generated data are logged in to the database as text file with proper indication about the status of the process variable (normal or not normal. Three different types of boiler drum level control system are designed in the Circuit Design and Simulation toolkit of LabVIEW. This work provides the knowledge about the Fuzzy Adaptive PID Controller and the various PID controller design methods such as Zeigler-Nichol method, Tyreus-Luyben method, Internal Model Control (IMC). Comparative study is made on the performance of the PID and Fuzzy Adaptive PID controller for better control system design. The internet plays a significant and vital role in the real time control and monitoring of the industrial process. Internet based system control and monitor the plant system remotely from anywhere without any limitation to any geographical region. Internet based boiler control system is developed by a Web Publishing tool in LabVIEW. The use of internet as a communication medium provides the flexible and cost- effective solution. Now, to analyse the performance of boiler drum level control system, Internet based data logging and supervisory control system is designed. Hence, anyone can control and monitor the boiler plant globally

Intelligent methods for complex systems control engineering

This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions

OPERATION AND PROCESS CONTROL DEVELOPMENT FOR A PILOT-SCALE LEACHING AND SOLVENT EXTRACTION CIRCUIT RECOVERING RARE EARTH ELEMENTS FROM COAL-BASED SOURCES

The US Department of Energy in 2010 has identified several rare earth elements as critical materials to enable clean technologies. As part of ongoing research in REEs (rare earth elements) recovery from coal sources, the University of Kentucky has designed, developed and is demonstrating a ¼ ton/hour pilot-scale processing plant to produce high-grade REEs from coal sources. Due to the need to control critical variables (e.g. pH, tank level, etc.), process control is required. To ensure adequate process control, a study was conducted on leaching and solvent extraction control to evaluate the potential of achieving low-cost REE recovery in addition to developing a process control PLC system. The overall operational design and utilization of Six Sigma methodologies is discussed. Further, the application of the controls design, both procedural and electronic for the control of process variables such as pH is discussed. Variations in output parameters were quantified as a function of time. Data trends show that the mean process variable was maintained within prescribed limits. Future work for the utilization of data analysis and integration for data-based decision-making will be discussed