Terrence L. Blevins

Bio: Terrence L. Blevins is an academic researcher from Emerson Electric. The author has contributed to research in topics: Process control & Process (computing). The author has an hindex of 39, co-authored 177 publications receiving 4695 citations.

Diagnostic expert in a process control system

Abstract: A diagnostic system for use in a process control system collects and stores in a database information pertaining to the operation of the process control system, and that uses an expert engine to apply rules for analysis to the information in the database to determine solutions to problems. The database stores various types of information such as event and alarm data, notices of scheduled maintenance and changes to operating parameters, and historical data related to previous changes to the process control system that are relevant to determining both the source of the problems detected in the process control system and the steps necessary to either further analyze or correct the detected problems. The diagnostic system identifies the source of the problem and identifies and runs the appropriate analytical tools or takes remedial measures based on the rules for analysis for the expert engine.

Diagnostics in a process control system

Abstract: A diagnostic tool automatically collects and stores data indicative of a variability parameter, a mode parameter, a status parameter and a limit parameter associated with each of the different devices, loops or function blocks within a process control system, processes the collected data to determine which devices, loops or function blocks have problems that result in reduced performance of the process control system, displays a list of detected problems to an operator and then suggests the use of other, more specific diagnostic tools to further pinpoint or correct the problems. When the diagnostic tool recommends and executes a data intensive application as the further diagnostic tool, it automatically configures a controller of the process control network to collect the data needed for such a tool.

Integrated model predictive control and optimization within a process control system

Abstract: An integrated optimization and control technique integrates an optimization procedure, such as a linear or quadratic programming optimization procedure, with advanced control, such as model predictive control, within a process plant in which the number of control and auxiliary variables can be greater than the number of manipulated variables within the process plant. The technique first determines a step response matrix defining the correlation between changes in the manipulated variables and each of the process variables that are used during optimization. A subset of the control variables and auxiliary variables is then selected to be used as inputs to a model predictive control routine used to perform control during operation of the process and a square M by M control matrix to be used by the model predictive control routine is generated. Thereafter, during each scan of the process controller, the optimizer routine calculates the optimal operating target of each of the complete set of control and auxiliary variables and provides the determined target operating points for each of the selected subset of control and auxiliary variables to the model predictive control routine as inputs. The model predictive control routine determines changes in the manipulated variables for use in controlling the process from the target and measured values for each of the subset of the control and auxiliary variables and the M by M control matrix.

Integrated, advanced control blocks in process control systems

Abstract: An advanced control block that implements multiple-input/multiple-output control, such as model predictive control, within a process control system is initiated by creating an initial control block having generic control logic and desired control inputs and control outputs communicatively connected to process outputs and process inputs within a process control routine. A waveform generator within the control block systematically upsets each of the process inputs via the control block outputs using excitation waveforms designed for use in developing a process model. At the same time, a data collection routine collects data indicating the response of each of the process outputs to the waveforms delivered at each of the process inputs. After sufficient data has been collected, a process modeling routine generates a process model from the collected data and a control logic parameter creation routine creates control logic parameters for the control logic from the process model. The control logic parameters and the process model are then downloaded to the control block to complete formation of the advanced control block. Thereafter, the advanced control block is used to provide advanced process control within the process control routine. Likewise, the process model is used to provide simulation of the process or to produce virtual process outputs.

Process plant monitoring based on multivariate statistical analysis and on-line process simulation

Abstract: Disclosed are systems and methods for on-line monitoring of operation of a process in connection with process measurements indicative of the operation of the process. In some cases, the operation of the process is simulated to generate model data indicative of a simulated representation of the operation of the process and based on the process measurements. A multivariate statistical analysis of the operation of the process is implemented based on the model data and the process measurements. The output data from the multivariate statistical analysis may then be evaluated during the operation of the process to enable the on-line monitoring of the process involving, for instance, fault detection via classification analysis of the output data.

Multiple Imputation for Nonresponse in Surveys

Abstract: 25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

Abstract: Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.

WirelessHART: Applying Wireless Technology in Real-Time Industrial Process Control

Abstract: Wireless technology has been regarded as a paradigm shifter in the process industry. The first open wireless communication standard specifically designed for process measurement and control applications, WirelessHART was officially released in September 2007 (as a part of the HART 7 Specification). WirelessHART is a secure and TDMA- based wireless mesh networking technology operating in the 2.4 GHz ISM radio band. In this paper, we give an introduction to the architecture of WirelessHART and share our first-hand experience in building a prototype for this specification. We describe several challenges we had to tackle during the implementation, such as the design of the timer, network wide synchronization, communication security, reliable mesh networking, and the central network manager. For each challenge, we provide a detailed analysis and propose our solution. Based on the prototype implementation, a simple WirelessHART network has been built for the purpose of demonstration. The demonstration network in turn validates our design. To the best of our knowledge, this is the first reported effort to build a WirelessHART protocol stack.