The overall, coordinated management of different operational tasks in a chemical plant can improve operational efficiency. These operational tasks can be hierarchically categorised, from the lowest to highest level, as: data acquisition; regulatory control; monitoring; data reconciliation; fault detection and diagnosis; supervisory control; scheduling; and planning. Although each of these tasks is responsible for a particular function, they are dependent on each other, which is why an approach wherein all the different tasks can be integrated into a single unified framework is desirable. While integration has important benefits such as a significant reduction in operator workload and improved decision making, its realisation presents considerable challenges. Few previous works have addressed this topic and even fewer have investigated recent computing paradigms which may greatly assist in the development of a unifying framework. Multi-agent systems were introduced and investigated in this study as a possible means for achieving integration of operational tasks in chemical plants. Multi-agent systems are the subject of a sub-field of computing research known as agent-based computing. Agent-based computing represents a relatively recent and powerful high-level computing paradigm. Initially, a number of software applications were developed for the purposes of this study to assist realisation of the operational tasks. To simplify the process of system development and provide guidance for those unfamiliar with multi-agent systems wishing to adopt the proposed technique, an extensive methodology was devised. The operational tasks were then integrated using the proposed methodology to form an integrated multi-agent system, with the pilot plant at Murdoch University being used as a test base for the solution. The results were positive and demonstrated that the proposed agent-based solution was able to effectively account for the pilot plant setting. It was concluded that, in addition to presently available integration techniques and base technologies, the agent-based approach to integration of operational tasks in chemical plants presents a viable alternative solution.

Zugl.: Zürich, Swiss Banking School, Diplomarbeit. / Literaturverz.

Zugl.: Danzig, Techn. Univ., Diss., 2007

Thesis (Ph. D.)--University of Chicago, 1933. / Vita. Lithoprinted. "Private edition, distributed by the University of Chicago libraries, Chicago, Illinois."

Thesis (M. S.)--Aerospace Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Mavris, Dimitri; Committee Member: Bishop, Carlee; Committee Member: Brown, David; Committee Member: Costello, Mark; Committee Member: Schrage, Daniel.

Zugl.: Danzig, Techn. Univ., Diss., 2007.

Thesis (PH. D.) - University of Michigan, 1942. / Two articles reprinted from the Journal of applied physics, v. 14, no. 11, Nov. 1943 and v. 15, no. 5, May, 1944. Bibliographical footnotes.

Zugl.: Bremen, Univ., Diss., 2006.

The delivery of medium and mega project has been a challenge for a number of decades, with attempts made to reduce the associated issues around engineering projects implementation. Project delivery before 1950 mainly relates to cost, time, and scope, with a lack of documentation pertaining to methods, as well as inadequate techniques to achieve a quality final product. The concept of Asset Operational Readiness (AOR) emanates in the 1950s from the military as means of providing the “developmental state of weapons systems”. The concept gained momentum as it was associated with “system safety” in the 1980s for decision-making. AOR can be defined as an establishment of a state or configuration which, after completion of the project, “places the right people in the right places at a right time working with the right hardware according to the right procedures and management controls”. The research work covered in this thesis, aims to propose a best-practice AOR framework for mega-projects in the power generation industry. A thorough Literature Review provides an overview of best practices on the AOR requirements for various industrial fields. The survey shows that AOR implementation follows the Project Life Cycle Management (PLCM) principles, from conceptual and pre-feasibility phases to commissioning and operation phases. In addition, the survey considers methodologies and techniques, which aids to enhance AOR framework development such as Root Cause Analysis (RCA) exercises. The study has provided an opportunity to develop an AOR theoretical framework refinement methodology, inclusive of RCA, AOR assessment tools, qualitative survey tool, and scoring systems. The AOR best practice framework and refinement methodology application to a real mega project case study, with historical data, enables a stage wise assessment of each component for individualized performance rating. This provides an identification of the areas that require refinement to have an improved AOR framework as outcome. The research outcome shows that there are implications for inadequate development and implementation of items in the proposed framework. The implications range from rework during manufacturing and construction, poor product quality delivery, poor performance post commissioning, and overall cost overruns. In addition, the study provides evidence that implementation of the AOR framework aids a project to realize its potential and yield positive results, which ultimately benefits an organization in terms of quality product delivery, cost reduction, and optimal Operations and Maintenance of the established asset. / Dissertation (MEng)--University of Pretoria, 2019. / Civil Engineering / MEng / Unrestricted

For the mental and physical wellbeing of nuclear power plant (NPP) reactor operators (ROs) it is pertinent these work environments take advantage of automation, to an appropriate extent, to reduce workload and increase performance. With automation, RO resources can be better distributed to make sure NPP operations are running smoothly and efficiently. However, inappropriate automation may put ROs at risk of becoming complacent and slow to react, thus unable to perform their job in emergency situations. In this study students acted as NPP ROs and interacted with different tasks and levels of automation. Since NPPs are becoming more digitalized it is important to understand how these changes are going to affect operators' performance and perceived mental workload (MW). Individual differences are also considered, as not everyone is going to have the same reaction to these changes. Results of this study indicate that an increase in automation decreases time to react to the automation requesting input. However, there were significant differences between perceived MW such that higher MW was reported in the higher level of automation for checking and responding tasks. Personality traits can play a large role in how ROs respond to and work with automation. In this study, personality (i.e., Big 5) was not correlated to any MW measures but was positively correlated with perception of automation competence and usefulness in the lower automation condition. When compared with previous iterations of this study that had no automation, both low and high LOA significantly reduced perceived workload. This study's findings enhance awareness of individual differences and their implications on ROs' perceived MW and automation adoption and the importance of upcoming changes in NPPs to ensure optimized RO vigilance and performance.