Home About us Contact
|
Home About us Contact | ||
|
|
||
Industrial Data (industrial + data)
Selected AbstractsContinuous Soluble Ziegler-Natta Ethylene Polymerizations in Reactor Trains, 2 , Estimation of Kinetic Parameters from Industrial DataMACROMOLECULAR REACTION ENGINEERING, Issue 2 2008Marcelo Embiruçu Abstract We show that it is possible to estimate kinetic parameters for complex mechanistic polymerization models from available industrial data. A methodology is developed for efficient handling and reconciliation of industrial data and is then applied to allow estimation of kinetic parameters for industrial ethylene polymerizations performed in reactor trains using soluble Ziegler-Natta catalysts. The parameter estimation procedure is formulated as a nonlinear optimization procedure subject to hard and soft model and process constraints. Parameter estimates obtained for the catalyst system allow a very good description of actual industrial data used during the estimation process and also allow very good prediction of process performance when completely new operating conditions are considered. It is concluded that complex phenomenological models can be successfully fitted to actual industrial processes without the need to carry out extensive experimental tests in the laboratory. [source] How Does Structural Reform Affect Regional Development?ECONOMIC GEOGRAPHY, Issue 4 2000Resolving Contradictory Theory with Evidence from India Abstract: Regional theory offers little coherent guidance on the prospects for interregional development after structural reform in developing nations. In this paper I suggest a basic set of hypotheses in which the neoliberal nation-state is simultaneously a reduced state (less concerned about promoting regional balance) and an enlarged state (directing development toward selected regions). Under the new regulatory structure the location of post-reform investments may be expected to favor the coast, advanced regions, and existing metropolises (especially the edge areas); these expectations may be more true for foreign direct investments than domestic investments (especially the direct investments of the state). I use disaggregated pre- and post-reform industrial data from India to test the hypotheses. The results offer partial to full support for all hypotheses, providing evidence of the return of cumulative causation, and raising concerns about the political economy of future development in the lagging regions. [source] Analysis of multivariable controllers using degree of freedom dataINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 7-9 2003T. J. Harris Abstract Most approaches for monitoring, diagnosis and performance analysis of multivariable control loops employ time series methods and use non-parametric statistics to analyse the process inputs and outputs. In this paper, we explore the use of a discrete variable that summarizes the status of the constraint set of the controller to analyse the long run behaviour of control systems. We introduce a number of waiting and holding time statistics that describe the status of this data, which we call the degree of freedom data. We demonstrate how Markov Chains might be used to model the status of the degree of freedom data. This model-based approach has the potential to provide considerable insight into the behaviour of a model based control scheme with relative ease. We demonstrate the methodologies on simulated and industrial data. Copyright © 2003 John Wiley & Sons, Ltd. [source] SIMULATION OF THIN-FILM DEODORIZERS IN PALM OIL REFININGJOURNAL OF FOOD PROCESS ENGINEERING, Issue 2010ROBERTA CERIANI ABSTRACT As the need for healthier fats and oils (natural vitamin and trans fat contents) and interest in biofuels are growing, many changes in the world's vegetable oil market are driving the oil industry to developing new technologies and recycling traditional ones. Computational simulation is widely used in the chemical and petrochemical industries as a tool for optimization and design of (new) processes, but that is not the case for the edible oil industry. Thin-film deodorizers are novel equipment developed for steam deacidification of vegetable oils, and no work on the simulation of this type of equipment could be found in the open literature. This paper tries to fill this gap by presenting results from the study of the effect of processing variables, such as temperature, pressure and percentage of stripping steam, in the final quality of product (deacidified palm oil) in terms of final oil acidity, the tocopherol content and neutral oil loss. The simulation results have been evaluated by using the response surface methodology. The model generated by the statistical analysis for tocopherol retention has been validated by matching its results with industrial data published in the open literature. PRACTICAL APPLICATIONS This work is a continuation of our previous works (Ceriani and Meirelles 2004a, 2006; Ceriani et al. 2008), dealing with the simulation of continuous deodorization and/or steam deacidification for a variety of vegetable oils using stage-wised columns, and analyzing both the countercurrent and the cross-flow patterns. In this work, we have studied thin-film deodorizers, which are novel equipment developed for steam deacidification of vegetable oils. Here, we highlight issues related to final oil product quality and the corresponding process variables. [source] Development of a new soft sensor method using independent component analysis and partial least squaresAICHE JOURNAL, Issue 1 2009Hiromasa Kaneko Abstract Soft sensors are used widely to estimate a process variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants. To cope with this problem, a regression model can be updated. However, if the model is updated with an abnormal sample, the predictive ability can deteriorate. We have applied the independent component analysis (ICA) method to the soft sensor to increase fault detection ability. Then, we have tried to increase the predictive accuracy. By using the ICA-based fault detection and classification model, the objective variable can be predicted, updating the PLS model appropriately. We analyzed real industrial data as the application of the proposed method. The proposed method achieved higher predictive accuracy than the traditional one. Furthermore, the nonsteady state could be detected as abnormal correctly by the ICA model. © 2008 American Institute of Chemical Engineers AIChE J, 2009 [source] Continuous Soluble Ziegler-Natta Ethylene Polymerizations in Reactor Trains, 2 , Estimation of Kinetic Parameters from Industrial DataMACROMOLECULAR REACTION ENGINEERING, Issue 2 2008Marcelo Embiruçu Abstract We show that it is possible to estimate kinetic parameters for complex mechanistic polymerization models from available industrial data. A methodology is developed for efficient handling and reconciliation of industrial data and is then applied to allow estimation of kinetic parameters for industrial ethylene polymerizations performed in reactor trains using soluble Ziegler-Natta catalysts. The parameter estimation procedure is formulated as a nonlinear optimization procedure subject to hard and soft model and process constraints. Parameter estimates obtained for the catalyst system allow a very good description of actual industrial data used during the estimation process and also allow very good prediction of process performance when completely new operating conditions are considered. It is concluded that complex phenomenological models can be successfully fitted to actual industrial processes without the need to carry out extensive experimental tests in the laboratory. [source] Robust optimization for multiple responses using response surface methodologyAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 2 2010Zhen He Abstract Typically in the analysis of industrial data for product/process optimization, there are many response variables that are under investigation at the same time. Robustness is also an important concept in industrial optimization. Here, robustness means that the responses are not sensitive to the small changes of the input variables. However, most of the recent work in industrial optimization has not dealt with robustness, and most practitioners follow up optimization calculations without consideration for robustness. This paper presents a strategy for dealing with robustness and optimization simultaneously for multiple responses. In this paper, we propose a robustness desirability function distinguished from the optimization desirability function and also propose an overall desirability function approach, which makes balance between robustness and optimization for multiple response problems. Simplex search method is used to search for the most robust optimal point in the feasible operating region. Finally, the proposed strategy is illustrated with an example from the literature. Copyright © 2009 John Wiley & Sons, Ltd. [source] Estimation of Kinetic Parameters for Hydrogenation Reactions Using a Genetic AlgorithmCHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 10 2009A. Kadiva Abstract The kinetics of acetylene hydrogenation in a fixed-bed reactor of a commercial Pd/Al2O3 catalyst has been studied. The hydrogenation reactor considered in this work is an essential part of a vinyl chloride monomer (VCM) plant. Three well-known kinetic models were used to simulate the hydrogenation reactor under industrial operating conditions. Since none of the models provide appropriate prediction, the industrial data and calculated values were compared and optimum kinetic parameters were evaluated utilizing a genetic algorithm (GA) technique. The best kinetic parameters for the three models were determined under specified industrial operating conditions. The hydrogenation reactor was simulated using the estimated optimum kinetic parameters of the three models. Simulation results from the three models were compared to industrial data and the best kinetic model was found. This kinetic model with the evaluated optimum kinetic parameters can well predict the behavior of the industrial hydrogenation reactor to improve the performance of the process. [source] | ||||||||||