AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

Development of dynamic model and control techniques for microelectromechanical gyroscopes

In this thesis we investigate the effects of stiffness, damping and temperature on the performance of a MEMS vibratory gyroscope. The stiffness and damping parameters are chosen because they can be appropriately designed to synchronize the drive and sense mode resonance to enhance the sensitivity and stability of MEMS gyroscope. Our results show that increasing the drive axis stiffness from its tuned value by 50%, reduces the sense mode magnitude by ~27% and augments the resonance frequency by ~21%. The stiffness and damping are mildly sensitive to typical variations in operating temperature. The stiffness decreases by 0.30%, while the damping increases by 3.81% from their initial values, when the temperature is raised from -40 to 60C. Doubling the drive mode damping from its tuned value reduces the oscillation magnitude by 10%, but ~0.20% change in the resonance frequency. The predicted effects of stiffness, damping and temperature can be utilized to design a gyroscope for the desired operating condition

Review of Health Prognostics and Condition Monitoring of Electronic Components

To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted

Integration and optimal control of microcsp with building hvac systems: Review and future directions

Heating, ventilation, and air-conditioning (HVAC) systems are omnipresent in modern buildings and are responsible for a considerable share of consumed energy and the electricity bill in buildings. On the other hand, solar energy is abundant and could be used to support the building HVAC system through cogeneration of electricity and heat. Micro-scale concentrated solar power (MicroCSP) is a propitious solution for such applications that can be integrated into the building HVAC system to optimally provide both electricity and heat, on-demand via application of optimal control techniques. The use of thermal energy storage (TES) in MicroCSP adds dispatching capabilities to the MicroCSP energy production that will assist in optimal energy management in buildings. This work presents a review of the existing contributions on the combination of MicroCSP and HVAC systems in buildings and how it compares to other thermal-assisted HVAC applications. Different topologies and architectures for the integration of MicroCSP and building HVAC systems are proposed, and the components of standard MicroCSP systems with their control-oriented models are explained. Furthermore, this paper details the different control strategies to optimally manage the energy flow, both electrical and thermal, from the solar field to the building HVAC system to minimize energy consumption and/or operational cost

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Multivariable System Identification of a Continuous Binary Distillation Column

Distillation is a process that is commonly used in industries for separation purpose. A distillation column is a multivariable system which shows nonlinear dynamic behavior due to its nonlinear vapor-liquid equilibrium. In order to gain better product quality and lower energy consumption of the distillation column, an effective model based control system is needed to allow the process to be operated over a certain operating range. In control engineering, System Identification is considered as a well suited approach for developing an approximate model for the nonlinear system. In this study, System Identification technique is applied to predict the top and bottom product composition by focusing the temperature of the distillation column. The process in the column is based on the distillation of a binary mixture of Isopropyl Alcohol and Acetone. The experimental data obtained from the distillation column was used for estimation and validation of simulated models. During analysis, different types of linear and nonlinear models were developed and are compared to predict the best model which can be effectively used for designing the control system of the distillation column. Among the linear models such as; Autoregressive with Exogenous Input (ARX), Autoregressive Moving Average with Exogenous inputs (ARMAX), Linear State Space (LSS) model and Continuous Process Model were developed and compared with each other. The results of this comparison reveals that the perf01mance of LSS model is efficient and hence it was further used to improve the modeling approach and compared with other nonlinear models. A Nonlinear State Space (NSS) model was developed by the combination of LSS and Neural Network (NN) and is compared solely with NN and ANFIS identification model. The simulation results show that the developed NSS model is well capable of defining the dynan1ics of the plant based on the best fit criteria and residual performance. In addition to this, NSS model predicted the best statistical measurement of the nonlinear system. This approach is helpful for designing the efficient control system for online separation process of the plant