Unplanned dilution and ore-loss optimisation in underground mines via cooperative neuro-fuzzy network

The aim of study is to establish a proper unplanned dilution and ore-loss (UB: uneven break) management system. To achieve the goal, UB prediction and consultation systems were established using artificial neural network (ANN) and fuzzy expert system (FES). Attempts have been made to illuminate the UB mechanism by scrutinising the contributions of potential UB influence factors. Ultimately, the proposed UB prediction and consultation systems were unified as a cooperative neuro fuzzy system

Making and breaking power laws in evolutionary algorithm population dynamics

Deepening our understanding of the characteristics and behaviors of population-based search algorithms remains an important ongoing challenge in Evolutionary Computation. To date however, most studies of Evolutionary Algorithms have only been able to take place within tightly restricted experimental conditions. For instance, many analytical methods can only be applied to canonical algorithmic forms or can only evaluate evolution over simple test functions. Analysis of EA behavior under more complex conditions is needed to broaden our understanding of this population-based search process. This paper presents an approach to analyzing EA behavior that can be applied to a diverse range of algorithm designs and environmental conditions. The approach is based on evaluating an individual’s impact on population dynamics using metrics derived from genealogical graphs.\ud From experiments conducted over a broad range of conditions, some important conclusions are drawn in this study. First, it is determined that very few individuals in an EA population have a significant influence on future population dynamics with the impact size fitting a power law distribution. The power law distribution indicates there is a non-negligible probability that single individuals will dominate the entire population, irrespective of population size. Two EA design features are however found to cause strong changes to this aspect of EA behavior: i) the population topology and ii) the introduction of completely new individuals. If the EA population topology has a long path length or if new (i.e. historically uncoupled) individuals are continually inserted into the population, then power law deviations are observed for large impact sizes. It is concluded that such EA designs can not be dominated by a small number of individuals and hence should theoretically be capable of exhibiting higher degrees of parallel search behavior

Real time optimization of chemical processes

Due to current changes in the global market with increasing competition, strict bounds on product specifications, pricing pressures, and environmental issues, the chemical process industry has a high demand for methods and tools that enhance profitability by reducing the operating costs using limited resources. Real time optimization (RTO) strategies combine process control and economics, and have gone through much advancement during the last few decades. A typical real time optimization application is model based and requires the solution of at least three (usually) nonlinear programming problems, such as combined gross error detection and data reconciliation, parameter estimation and economic optimization. A successful implementation of RTO requires fast and accurate solution of these stated nonlinear programming problems.Current real time optimization strategies wait for steady state after a disturbance enters the process. If, during this wait, another disturbance enters into the system, it will increase the transition time significantly. An alternative, real time evolution (RTE), calculates the new set-points using only disturbance information and the new set-points are implemented in small step changes to a supervisory control system such as model predictive control (MPC) or can be implemented directly to the regulatory control layer. RTE ignores the important part of data screening therefore there is no surety that the calculated set-points represents current plant conditions. The main contribution of this thesis is to investigate the possibility of implementing new set-points without waiting for steady state. Two case studies, the Williams-Otto reactor and an integrated plant (the Williams-Otto reactor extended to include flash drum and large recycle stream), were used for analysis. The application of RTE, RTO and MPC were discussed and compared for the case studies to evaluate the performance in terms of the theoretical profit achieved.A new strategy, dynamic-RTO (D-RTO), based on modified dynamic data reconciliation (DDR) strategy and translated steady state model, was also developed for systems with significant bias and process noise. In the D-RTO strategy, the residual terms of the steady state model were calculated from the reconciled values. These residual terms were translated subsequently into the steady state model. Due to the translation there is no need for calculating set-point changes in small steps. The formulation of the DDR strategy is based on control vector parameterization techniques. D-RTO was compared with RTE and RTO for the two case studies. The results obtained show that RTE can lead to an unstable control if used without taking into account process and controller dynamics. For measurements having bias, the DDR strategy can be used with the assumption that the variables with bias are unmeasured and are calculated implicitly. The D-RTO strategy is able to deal with constant and changing bias, and is able to decrease profit losses during transitions. D-RTO is a good alternative to steady state RTO, for processes with frequent disturbances, where RTO implementation due to its steady state nature may not be justifiable

Implementation and performance assessment of a real-time optimization system on a virtual fluidized-bed catalytic-cracking plant

This thesis develops and evaluates RTO implementation in a FCCU virtual plant, taking into account each RTO stage (noise elimination, steady-state detection, data validation, parameter estimation, and optimization). The dynamic data to carry out this analysis were obtained from an FCCU virtual plant based on a dynamic deterministic model developed in Matlab®. The model output data were contaminated with Gaussian and gross errors to simulate measurements from a real plant. For denoising, steady-state detection, data reconciliation, parameter estimation, and optimization, different strategies and algorithms were studied and assessed, while a decentralized PID was proposed for the control system. Finally, the most appropriate strategies for the case study were implemented and their performance was fully evaluated.Resumen: Esta tesis desarrolla y evalúa la implementación de la RTO en una planta virtual de FCCU, teniendo en cuenta cada etapa de una RTO (eliminación de ruido, detección de estado estable, validación de datos, estimación de parámetros y optimización). Los datos dinámicos para llevar a cabo este análisis se obtuvieron de una planta virtual de FCCU basada en un modelo determinista dinámico desarrollado en Matlab®. Los datos de salida del modelo se contaminaron con error de Gauss y error grueso para simular mediciones de una planta real. Para la eliminación de ruido, la detección de estado estable, la reconciliación de datos, la estimación de parámetros y la optimización, se estudiaron y evaluaron diferentes estrategias y algoritmos, mientras que para el sistema de control se propuso un PID descentralizado. Finalmente, se implementaron las estrategias más apropiadas para el estudio de caso y se evaluó su desempeño en conjunto.Maestrí

Overview of cogeneration at LSU

Cogeneration (or Combined Heat and Power) continues to gain importance in power production because of its high efficiency, environmental friendliness, and flexibility. Louisiana State University (LSU) recently began operation of a new 20 MW cogeneration system. This new facility can serve as a useful learning tool for chemical and mechanical engineering students throughout their education at LSU. The goal of this project is to develop educational modules utilizing the cogeneration system which have industrial significance. Educational modules will include: a comparison of ideal gas versus real gas thermodynamics for a cogeneration optimization problem, a cogeneration data reconciliation problem, and a system level energy management optimization problem. The modules will be solved using Microsoft Excel as a solution platform to help promote wide spread use. The energy management strategy accounts for seasonal and time of day operating strategies. The optimal operating strategy is compared to current operating strategies to determine the most economical and most efficient methods of operating the LSU utility system. The new operating strategies can offer significant potential savings

Integrated Model-Centric Decision Support System for Process Industries

To bring the advances in modeling, simulation and optimization environments (MSOEs), open-software architectures, and information technology closer to process industries, novel mechanisms and advanced software tools must be devised to simplify the definition of complex model-based problems. Synergistic interactions between complementary model-based software tools must be refined to unlock the potential of model-centric technologies in industries. This dissertation presents the conceptual definition of a single and consistent framework for integrated process decision support (IMCPSS) to facilitate the realistic formulation of related model-based engineering problems. Through the integration of data management, simulation, parameter estimation, data reconciliation, and optimization methods, this framework seeks to extend the viability of model-centric technologies within the industrial workplace. The main contribution is the conceptual definition and implementation of mechanisms to ease the formulation of large-scale data-driven/model-based problems: data model definitions (DMDs), problem formulation objects (PFOs) and process data objects (PDOs). These mechanisms allow the definition of problems in terms of physical variables; to embed plant data seamlessly into model-based problems; and to permit data transfer, re-usability, and synergy among different activities. A second contribution is the design and implementation of the problem definition environment (PDE). The PDE is a robust object-oriented software component that coordinates the problem formulation and the interaction between activities by means of a user-friendly interface. The PDE administers information contained in DMD and coordinates the creation of PFOs and PIFs. Last, this dissertation contributes a systematic integration of data pre-processing and conditioning techniques and MSOEs. The proposed process data management system (pDMS) implements such methodologies. All required manipulations are supervised by the PDE, which represents an important advantage when dealing with high volumes of data. The IMCPSS responds to the need for software tools centered in process engineers for which the complexity of using current modeling environments is a barrier for broader application of model-based activities. Consequently, the IMCPSS represents a valuable tool for process industries, as the facilitation of problem formulation is translated into incorporation of plant data in less error-prone manner, maximization of time dedicated to the analysis of processes, and exploitation of synergy among activities based on process models

Modelling and data validation for the energy analysis of absorption refrigeration systems

Data validation and reconciliation techniques have been extensively used in the process industry to improve the data accuracy. These techniques exploit the redundancy in the measurements in order to obtain a set of adjusted measurements that satisfy the plant model. Nevertheless, not many applications deal with closed cycles with complex connectivity and recycle loops, as in absorption refrigeration cycles. This thesis proposes a methodology for the steady-state data validation of absorption refrigeration systems. This methodology includes the identification of steady-state, resolution of the data reconciliation and parameter estimation problems and the detection and elimination of gross errors. The methodology developed through this thesis will be useful for generating a set of coherent measurements and operation parameters of an absorption chiller for downstream applications: performance calculation, development of empirical models, optimisation, etc. The methodology is demonstrated using experimental data of different types of absorption refrigeration systems with different levels of redundancy.Los procedimientos de validación y reconciliación de datos se han utilizado en la industria de procesos para mejorar la precisión de los datos. Estos procedimientos aprovechan la redundancia enlas mediciones para obtener un conjunto de datos ajustados que satisfacen el modelo de la planta. Sin embargo, no hay muchas aplicaciones que traten con ciclos cerrados, y configuraciones complejas, como los ciclos de refrigeración por absorción. Esta tesis propone una metodología para la validación de datos en estado estacionario de enfriadoras de absorción. Estametodología incluye la identificación del estado estacionario, la resolución de los problemas de reconciliación de datos y estimación de parámetrosy la detección de errores sistemáticos. Esta metodología será útil para generar un conjunto de medidas coherentes para aplicaciones como: cálculo de prestaciones, desarrollo de modelos empíricos, optimización, etc. La metodología es demostrada utilizando datos experimentales de diferentes enfriadoras de absorción, con diferentes niveles de redundancia

A Model-Based Framework for the Smart Manufacturing of Polymers

It is hard to point a daily activity in which polymeric materials or plastics are not involved. The synthesis of polymers occurs by reacting small molecules together to form, under certain conditions, long molecules. In polymer synthesis, it is mandatory to assure uniformity between batches, high-quality of end-products, efficiency, minimum environmental impact, and safety. It remains as a major challenge the establishment of operational conditions capable of achieving all objectives together. In this dissertation, different model-centric strategies are combined, assessed, and tested for two polymerization systems. The first system is the synthesis of polyacrylamide in aqueous solution using potassium persulfate as initiator in a semi-batch reactor. In this system, the proposed framework integrates nonlinear modelling, dynamic optimization, advanced control, and nonlinear state estimation. The objectives include the achievement of desired polymer characteristics through feedback control and a complete motoring during the reaction. The estimated properties are close to experimental values, and there is a visible noise reduction. A 42% improvement of set point accomplishment in average is observed when comparing feedback control combined with a hybrid discrete-time extended Kalman filter (h-DEKF) and feedback control only. The 4-state geometric observer (GO) with passive structure, another state estimation strategy, shows the best performance. Besides achieving smooth signal processing, the observer improves 52% the estimation of the final molecular weight distribution when compared with the h-DEKF. The second system corresponds to the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst in a semi-batch reactor. The evaluated operating conditions consider different diene concentrations and reaction temperatures. Initially, the nonlinear model is validated followed by a global sensitivity analysis, which permits the selection of the important parameters. Afterwards, the most important kinetic parameters are estimated online using an extended Kalman filter (EKF), a variation of the GO that uses a preconditioner, and a data-driven strategy referred as the retrospective cost model refinement (RCMR) algorithm. The first two strategies improve the measured signal, but fail to predict other properties. The RCMR algorithm demonstrates an adequate estimation of the unknown parameters, and the estimates converge close to theoretical values without requiring prior knowledge

New Hybrid Non-Dominated Sorting Differential Evolutionary Algorithm

This paper presents a new multi objective optimization algorithm with the aim of complete coverage, faster global convergence and higher solution quality. In this technique, the high-speed characteristic of particle swarm optimization (PSO) is combined with non-dominated differential evolutionary (NSDE) and an efficient multi objective optimization algorithm is created. This method posses high convergence characteristic in quite less execution times. Generating fewer populations to find the Pareto front also makes the proposed algorithm use less memory. For the purpose of performance evaluation, the algorithm is verified with four benchmarking functions on its global optimal search ability and compared with two recognized algorithm to assess its diversity. The capability of the suggested algorithm in solving practical engineering problems such as power system protection is also studied and the results are discussed in detail