Thesis (D.Eng.)--Cleveland State University, 2009. / Abstract. Title from PDF t.p. (viewed on Oct. 26, 2009). Includes bibliographical references (p. 78-89). Available online via the OhioLINK ETD Center and also available in print.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references. Also available via the Internet.

This thesis aims to establish methods for identifying and training the knowledge and skills of operating a novel automated system still undergoing final design and construction. The absence of operating experience requires the characteristics of the system to be examined so that the future tasks of supervisors can be anticipated in order to address human factors design. This work is carried out in the context of an 'Integrated Mail Processor' (IMP)—a highly automated letter sorting machine being developed by Royal Mail.

To date there has been significant research on the topic of financial distress prediction, due to its relevance to various stakeholders. Beaver (1966), Altman (1968) and Ohlson (1980) are generally regarded as the pioneers in this field of study, despite heavy criticism their models are widely accepted and used. Studies by Grice & Ingram (2001); Grice & Dugan (2001) and Sudarsanam & Taffler (1995) have shown that these models require to be updated regularly with new variables and coefficients due to various factors. This study proposes to add to the body of knowledge by developing a distress prediction model using a classic statistical method and financial ratios, calculated on published company data of organisations listed on the Johannesburg Stock Exchange. / Dissertation (MBA)--University of Pretoria, 2013. / zkgibs2014 / Gordon Institute of Business Science (GIBS) / MBA / Unrestricted

The aim of this project is to design and build an industrialized microprocessor system capable of testing the limits and capabilities of microprocessors in the industrial process control world. The system must be capable of operating in a data logging or control or supervisory capacity. The system consists of a ruggerdized, electrically isolated unit, designed on a "black box" principle, with minimum operator controls. It is housed in a sealed crate with internal access via rows of input and output plugs and connecters. The system has been designed on a modular basis in order to simplify expansion. It can be operated as a small dedicated controller or expanded by the addition of memory and/or industrial I/O modules to its full capacity. The system is based on an INTEL 8080 microprocessor. The industrial interface consists of electrically isolated analog and digital input and output modules which can be selected under program control. There are also up to 64 asynchronous priority encoded alarm channels that can interrupt the control sequence at any time should an alarm condition arise. For debugging hardware and software a plug-on front panel unit is provided.

Although there has been progress in the area of Multivariate Statistical Process Control (MSPC), there are numerous limitations as well as unanswered questions with the current techniques. MSPC charts plotting Hotelling's T2 require the normality assumption for the joint distribution among the process variables, which is not feasible in many industrial settings, hence the motivation to investigate nonparametric techniques for multivariate data in quality control. In this research, the goal will be to create a systematic distribution-free approach by extending current developments and focusing on the dimensionality reduction using Principal Component Analysis. The proposed technique is different from current approaches given that it creates a nonparametric control chart using robust simplicial depth ranks of the first and last set of principal components to improve signal detection in multivariate quality control with no distributional assumptions. The proposed technique has the advantages of ease of use and robustness in MSPC for monitoring variability and correlation shifts. By making the approach simple to use in an industrial setting, the probability of adoption is enhanced. Improved MSPC can result in a cost savings and improved quality.

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<p>Systematic strategies for optimal plant operation under partial shutdowns were developed. Partial shutdowns are circumscribed process unit shutdowns that permit the rest of the plant to continue operating to some degree. These strategies manipulate the degrees-of-freedom in a plant---during and after a shutdown---to restore plant production in a cost-optimal fashion, while meeting safety and operational constraints. This is accomplished through the adjustments of production rates, recycles and buffer levels.</p> <p>Our multi-tiered dynamic optimization approach allows for the prioritization of multiple objectives and the specification of trade-offs between these objectives. The solution of the optimization problem informs the formulation of inventory management policies. A Model-Predictive-Control (MPC) based partial shutdown algorithm implements these policies under feedback.</p> <p>Parsimonious discrete modeling formulations were presented for handling model discontinuities such as shutdown thresholds, induced shutdowns and minimum shutdown durations. The problem of minimizing restoration time was considered.</p> <p>We investigated the use of state/parameter estimation algorithms to moderate the effects of plant-model mismatch. The algorithms are based on novel configurations of the constrained Unscented Kalman Filter (UKF). Constraints on the estimates are enforced through a simple projection method. A dynamic feasibility tier ensures that terminal constraints and parameters are feasible for the prediction horizon in the control optimization problem.</p> <p>A modeling system (MLDO) was created for prototyping dynamic optimization models. It transforms a mathematical description of a model into code in various computer languages for the purposes of optimization, simulation, visualization and analysis of dynamic optimization problems. Facilities for problem reformulation and transformations are included.</p> / Doctor of Philosophy (PhD)

Submitted in fulfillment of the requirements for the Doctors Degree in Engineering (Electrical Engineering: Light Current), Durban University of Technology, Durban, South Africa, 2017. / Controller performance assessment (CPA) is concerned with the design of analytical tools that are utilized to evaluate the performance of process control loops. The objective of the CPA is to ensure that control systems operate at their full potential, and also to indicate when a controller design is performing unsatisfactorily under current closed loop conditions. Such monitoring efforts are imperative to minimize product variability, improve production rates and reduce wastage. Various studies conducted on process control loop performance indicate that as many as 60% of control loops often suffer from some kind of performance problem. It is therefore an important task to detect unsatisfactory control loop behavior and suggest remedial action. Such a monitoring system must be integrated into the control system life span as plant changes and hardware issues become apparent. CPA is well established for linear systems. However, not much research has been conducted on CPA for nonlinear systems. Traditional CPA analytical tools depend on the theoretical minimum variance control law that is derived from models of linear systems. In systems exhibiting dominant nonlinear behavior, the accuracy of linear based CPA is compromised. In light of this, there is a need to broaden existing CPA knowledge base with comprehensive benchmarking indices for the performance analysis of nonlinear process control systems. The research efforts presented in this thesis focuses on the development and analysis of such CPA tools for univariate nonlinear process control loops experiencing the negative effects of dominant nonlinearities emanating from the process. Two novel CPA frameworks are proposed; first a model based nonlinear assessment index is developed using an open loop model of the plant in an artificial neural network NARMAX (NNARMAX) representation. The nonlinear control loop is optimized offline using a proposed Nelder Mead-Particle Swarm Optimization (NM-PSO) hybrid search to determine global optimal control parameters for a gain scheduled PID controller. Application of the benchmark in real-time utilizes a synthetic process output derived from the NNARMAX system which is compared to the actual closed loop performance. In the case where no process model is available, a second method is presented. An autonomous data driven approach based on Multi-Class Support Vector Machines (MC- SVMs) is developed and analyzed. Unlike the model based method, the closed loop performance is classified according to five distinct class groups. MC-SVM classifier requires minimal process loop information other than routine operating closed loop data. Several simulation case studies conducted using MATLAB™ software package demonstrate the effectiveness of the proposed performance indices. Furthermore, the methodologies presented in this work were tested on real world systems using control loop data sets from a computer interfaced full scale pilot pH neutralization plant and pulp and paper industry. / D