In this thesis process control concepts were used to develop software that could be adapted to a real world situation. The software was used to control a simple temperature regulating experiment. This experiment was used to demonstrate the use of OPTOMUX analog and digital input/output devices in controlling a process. The goal of this experiment was to use the input/output devices in controlling the temperature of the box within specified tolerances for a designated period of time. To accomplish optimal use of equipment and optimal control, a mathematical model was derived to predict the behavior of the process under control. The pattern observed while the temperature was increasing toward room temperature closely resembled an exponential function. For temperatures above room temperatures the curve then approximated a square root function. The pattern followed when decreasing the temperature-was exponential throughout. The time required to collect all the significant data in the case of increasing the temperature was two hours. In the case of decreasing temperature, one hour. Beyond these time limits the temperature remained essentially constant. The maximum temperature value that could be reached was six degrees above room temperature and the minimum two degrees below room temperature.

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Hybrid systems combine the continuous behavior evolution specified by differential equations with discontinuous changes specified by discrete event logic. Usually these systems in the processing industry can be identified as having to depend on discrete decisions regarding their operation. In process control there therefore is a challenge to automate these decisions. A model predictive control (MPC) strategy was proposed and verified for the control of hybrid systems. More specifically, the dynamic matrix control (DMC) framework commonly used in industry for the control of continuous variables was modified to deal with mixed integer variables, which are necessary for the modelling and control of hybrid systems. The algorithm was designed and commissioned in a closed control loop comprising a SCADA system and an optimiser (GAMS). GAMS (General Algebraic Modelling System) is an optimisation package that is able to solve for integer/continuous variables given a model of the system and an appropriate objective function. Online and offline closed loop tests were undertaken on a benchmark interacting tank system and a heating/cooling circuit. The algorithm was also applied to an industrial problem requiring the optimal sequencing of coal locks in real time. To complete the research concerning controller design for hybrid behavior, an investigation was undertaken regarding systems that have different modes of operation due to physicochemical (inherent) discontinuities e.g. a tank with discontinuous cross sectional area, fitted with an overflow. The findings from the online tests and offline simulations reveal that the proposed algorithm, with some system specific modification, was able to control each of the four hybrid systems under investigation. Based on which hybrid system was being controlled, by modifying the DMC algorithm to include integer variables, the mixed integer predictive controller (MIPC) was employed to initiate selections, switchings and determine sequences. Control of the interacting tank system was focused on an optimum selection in terms of operating positions for process inputs. The algorithm was shown to retain the usual features of DMC (i.e. tuning and dealing with multivariable interaction). For a system with multiple modes of operation i.e. the heating/cooling circuit, the algorithm was able to switch the mode of operation in order to meet operating objectives. The MPC strategy was used to good effect when getting the algorithm to sequence the operation of several coal locks. In this instance, the controller maintained system variables within certain operating constraints. Furthermore, soft constraints were proposed and used to promote operation close to operating constraints without the danger of computational failure due to constraint violations. For systems with inherent discontinuities, a MPC strategy was proposed that predicted trajectories which crossed discontinuities. Convolution models were found to be inappropriate in this instance and state space equations describing the dynamics of the system were used instead. / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2002.

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, in fulfillment of the requirements for the degree of Masters in Engineering, 2017 / Manufacturing companies have challenges implementing business process improvements and innovations (BPI) on support service processes effectively and find it difficult to measure the impact of such interventions on the overall performance of the organisation. Measurement of the impact of BPIs on overall performance of manufacturing companies is problematic due to the inadequacy of BPI metrics for support services. Furthermore, there are no universally accepted frameworks available for the measurement of the impact of improvements on support service processes on the performance of manufacturing companies. While there are frameworks available for performance measurement in general, they are not specific to measurement of the impact of BPIs in manufacturing support service processes. An initial exploratory study, based on an online survey of 50 companies that would typically conduct BPI or where known to the researcher to have conducted BPIs recently, was conducted to explore the nature of BPIs in manufacturing support service processes in South Africa. A second longer online survey was then conducted with 1000 respondents in manufacturing companies selected through expert sampling to further explore the nature and impact of BPIs in manufacturing support service processes considering selection of support service processes, the types and number of support service processes as well as BPI traditions and methodologies in use within manufacturing companies. In addition, four companies were selected for in-depth case studies in which ten projects were analysed by applying within case and cross case analysis The results of the surveys, the case studies and a revisit to the case companies were used to refine successive iterations of a theoretical framework initially developed from the literature. The framework provides a set of guidelines and actions for manufacturing companies to effectively conduct BPIs on manufacturing support service processes a basis from which the impact of improvements in manufacturing support service processes on manufacturing companies can be measured by providing the measurement areas to consider and a set of high level measures to use as high level indicators. Finally, the framework was checked for completeness using recommended criteria derived from the literature and was found to be complete and suitable as it met all the criteria for good measurement systems defined in the literature sources used in this study. / MT 2017

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A dynamic job shop control system with a combined input/output control mechanism is developed for achieving improved shop performance. The problem is modelled such that at periodic intervals, the best combination of input and output variables for the forthcoming period are identified. The purpose of the control system is to determine the set of jobs to be released into the shop (input variables) and the capacity levels of machines (output variables) for a planning period such that a composite cost function is minimized. The problem is mathematically formulated as a 0-1 linear mixed integer program (MIP). An iterative based heuristic optimizing algorithm incorporating the MIP is developed. The control algorithm is compared with another job shop control system where only the input is assumed variable. The two systems are compared by computer simulation and results indicate significant improvements for most of the performance measures evaluated. Significant reductions in the mean and variance of the manufacturing lead time with a better distribution among its parts were also achieved. / Ph. D.

Indiana University-Purdue University Indianapolis (IUPUI) / Heating, ventilation, and air conditioning (HVAC) is the technology responsible to maintain temperature levels and air quality in buildings to certain standards. In a commercial setting, HVAC systems accounted for more than 50% of the total energy cost of the building in 2013 [13]. New control methods are always being worked on to improve the effectiveness and efficiency of the system. These control systems include model predictive control (MPC), evolutionary algorithm (EA), evolutionary programming (EP), and proportional-integral-derivative (PID) controllers. Such control tools are used on new HVAC system to ensure the ultimate efficiency and ensure the comfort of occupants. However, there is a need for a system that can monitor the energy performance of the HVAC system and ensure that it is operating in its optimal operation and controlled as expected. In this thesis, an air handling unit (AHU) of an HVAC system was modeled to analyze its performance using real data collected from an operating AHU using a wireless monitoring system. The purpose was to monitor the AHU's performance, analyze its key parameters to identify flaws, and evaluate the energy waste. This system will provide the maintenance personnel to key information to them to act for increasing energy efficiency. The mechanical model was experimentally validated first. Them a baseline operating condition was established. Finally, the system under extreme weather conditions was evaluated. The AHU's subsystem performance, the energy consumption and the potential wastes were monitored and quantified. The developed system was able to constantly monitor the system and report to the maintenance personnel the information they need. I can be used to identify energy savings opportunities due to controls malfunction. Implementation of this system will provide the system's key performance indicators, offer feedback for adjustment of control strategies, and identify the potential savings. To further verify the capabilities of the model, a case study was performed on an air handling unit on campus for a three month monitoring period. According to the mechanical model, a total of 63,455 kWh can be potentially saved on the unit by adjusting controls. In addition the mechanical model was able to identify other energy savings opportunities due to set point changes that may result in a total of 77,141 kWh.