Traditional simulation tools, such as simulators and simulation languages do not support intelligent simulation output analysis and offer little - or no - features to model intelligence within a system. However, in a modern manufacturing environment we often find ourselves facing high flexibility requirements and the need for quick response. We find an increasing number of artificial Intelligence (AI) applications on the shop floor and the need to figure out what is going on in our system - now, not later. Simulation is one tool which is useful to analyze manufacturing systems and make decisions based on the findings. Thus, it is desirable to be able to represent the intelligence we find on the shop floor in our simulation model, as well as an automated output analysis in order to speed up the decision making process. This paper describes the conceptual design and implementation of an AI architecture with the aim to offer a platform where both aspects, representation of intelligence in the system and outside the system (output analysis) can be performed. The architecture is based on Wang's extension (1995) of the simulation environment by Beaumariage (1990) which is implemented in an object-oriented environment. The object-oriented environment also offers an excellent means for implementing the Al architecture. To verify its usefulness two areas of application - priority sequencing at servers and material release - were implemented and several case studies carried out. / Graduation date: 1997

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A thesis submitted in compliance with the requirements for the Master's degree in Technology: Mechanical Engineering at Technikon Natal, 2002. / Existing CAD systems cannot satisfy all the requirements of 'real' design. Many designers would like to have more powerful and capable CAD systems. 'Real' design is a complex activity involving different types of problems thus a CAD system must be a general purpose system so that it can support all aspects of design including that of motion analysis. / M

published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy

Manufacturing intelligent agent simulation has not been widely applied in industry because of its application complexity. This complexity, which includes choosing priority machines or jobs, determining machine maintenance schedules, and allocating working shifts and breaks, requires intelligent decision making. Manufacturing systems are strongly influenced by intelligent decision makers. Especially for a fixed manufacturing layout, system performance improvement depends on intelligent manufacturing decision making. As a result, a manufacturing simulation can not be truly complete if intelligent decision making processes are not represented. This thesis describes an architecture which includes the representation of intelligent agents in manufacturing simulation model. An intelligent agent simulation environment (IASE) is developed under the concepts of distributed artificial intelligence and object oriented methodology. As an extension to an existing simulation environment, IASE inherits primary manufacturing simulation elements and material handling systems from object oriented manufacturing architecture (Beaumariage, 1990) and AGV simulation system (Beaumariage and Wang, 1995). In IASE, production operators, maintenance technicians and job releasers are created to represent manufacturing intelligent agents. Several basic elements such as the blackboard structure and knowledge base for supporting intelligent agent simulation are also developed. In contrast to traditional simulation environments designed for and in procedural programming languages, future extensions or modifications for IASE are eased since IASE is developed in an object oriented fashion. This paper introduces IASE structure both in the conceptual design and implementation methodology levels. At the end, two case studies are performed. The first case study is to verify IASE's implementation and results by comparing it with a model developed in SLAM II. The second case study, a mixed intelligent agent decision making example, demonstrates the intelligent agent simulation ability of IASE. / Graduation date: 1997

In Canada, the control of relative humidity is a key issue in greenhouse production as it has a direct and significant effect on dehumidification cost, crop quality and yield. Experiments were carried out to measure plant transpiration rate and fruit yield under four different ambient water vapour pressure deficits. Four identical greenhouses were used to produce tomatoes (Lycopersicon esculentum Mill.) under four different regimes of water vapour pressure deficit (VPD). Dehumidification costs were highly correlated to VPD: low VPD produced low transpiration requiring little dehumidification. Thus, managing plant transpiration can lead to a more efficient use of transpiration for crop production. A model was developed to optimise greenhouse climatic conditions to maximize net profit. The present project validated this model, and compared measured values with those calculated from the transpiration and condensation sub-models and from the entire model itself. The sub-models and entire model proved to be accurate within 3% when used to simulate ideal climatic conditions for periods of one week or longer. Model sensitivity was greatest for exterior temperature because this factor affects heating costs without increasing yields. Using winter climatic conditions typical of Quebec City, Canada, three greenhouse climate control strategies were simulated and compared with respect to energy consumption and yield of a tomato crop. The merit and drawback of each strategy are discussed.

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Thesis (M.S.)--Ohio University, June, 2007. / Title from PDF t.p. Includes bibliographical references.

The data assimilation process of adjusting variables in a reservoir simulation model to honor observations of field data is known as history matching and has been extensively studied for few decades. However, limited success has been achieved due to the high complexity of the problem and the large computational effort required by the practical applications. An automatic history matching module based on the ensemble Kalman filter is developed and validated in this dissertation. The ensemble Kalman filter has three steps: initial sampling, forecasting through a reservoir simulator, and assimilation. The initial random sampling is improved by the singular value decomposition, which properly selects the ensemble members with less dependence. In this way, the same level of accuracy is achieved through a smaller ensemble size. Four different schemes for the assimilation step are investigated and direct inverse and square root approaches are recommended. A modified ensemble Kalman filter algorithm, which addresses the preference to the ensemble members through a nonequally weighting factor, is proposed. This weighted ensemble Kalman filter generates better production matches and recovery forecasting than those from the conventional ensemble Kalman filter. The proposed method also has faster convergence at the early time period of history matching. Another variant, the singular evolutive interpolated Kalman filter, is also applied. The resampling step in this method appears to improve the filter stability and help the filter to deliver rapid convergence both in model and data domains. This method and the ensemble Kalman filter are effective for history matching and forecasting uncertainty quantification. The independence of the ensemble members during the forecasting step allows the benefit of high-performance computing for the ensemble Kalman filter implementation during automatic history matching. Two-level computation is adopted; distributing ensemble members simultaneously while simulating each member in a parallel style. Such computation yields a significant speedup. The developed module is integrated with reservoir simulators UTCHEM, GEM and ECLIPSE, and has been implemented in the framework Integrated Reservoir Simulation Platform (IRSP). The successful applications to two and three-dimensional cases using blackoil and compositional reservoir cases demonstrate the efficiency of the developed automatic history matching module.

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