Control of absorption columns in the bioethanol process: Influence of measurement uncertainties

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)The alcohol lost by evaporation during the bioethanol fermentation process may be collected and recovered using an absorption column. This equipment is also used in the carbonic gas treatment, a by-product from the sugar cane fermentation. In the present work. the development of nonlinear feed forward-feedback controllers, based on neural network inverse models, was proposed and tested to manipulate the absorbent flow rates The control purposes are to keep low ethanol concentration in the effluent gas phase from the first absorption column (ethanol recovery column), and to reduce the residual water concentration in the CO(2) gas effluent from the second tower (CO(2) treatment column). Based oil simulation studies, the neural network (ANN) controller performance was compared with the conventional PID control scheme application. The best ANN architecture was set up according to the Foresse and Hagan (1997) criterion. while the PID parameters were found from the well-known Cohen-Coon Equations and trial-and-error fine tuning. Initially, performances were evaluated for the system without concentration measurement uncertainties. From these tests, the ANN controller presented the smallest response time and overshoot for regulator and servo problems Three uncertainty levels were applied afterwards: 5%, 10%, and 15%. The ANN controller Outperformed the PID for all uncertainty levels tested for the ethanol recovery column. For the CO(2) treatment column. the ANN controller proceeded successfully under uncertainties of 5% and 10%, while the PID did not deal properly with uncertainties above 5% The statistical F-test, besides the ITAE, ISE, and IAE performance criteria, were calculated for both controllers applications and then compared They proved the superiority of the ANN control scheme. Using appropriately the proposed well-controlled absorption columns increases the efficiency of rile bioethanol production plant and can also provide carbon credits by avoiding CO(2) emission into the atmosphere (C) 2009 Elsevier Ltd All rights reservedO TEXTO COMPLETO DESTE ARTIGO, ESTARÁ DISPONÍVEL À PARTIR DE NOVEMBRO DE 2014.232271282Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPESP [2005/02536-9

Neural modeling helps the BOS process to achieve aimed end-point conditions in liquid steel

This paper describes the development of neural models and their industrial applications to the basic oxygen steel-making (BOS) plant of the Companhia Siderurgica Nacional (CSN-Volta Redonda/Brazil). The BOS is a transient process, highly complex and is also subject to oscillations in raw material composition. A precise model is essential to adjust end-blow oxygen and coolant requirements to match with the targets of end-point temperature and carbon percentage in liquid steel. An inverse neural model was developed in order to calculate the end-blow process adjustments. At the end of 40 industrial runs, 82.5% of simultaneous agreement with the targets was obtained, against 66% obtained from the commercial model usually employed at CSN's plant. The inverse model was then on-line implemented to automatically control the BOS process. The neural model has been retrained from previous weights and biases as soon as the performance decreases. Average hitting rate decreased related to the previous industrial investigation, however, it is still higher than that obtained from the commercial model application. As a consequence, liquid steel reprocessing is avoided and a high level of steel productivity is obtained. (c) 2005 Elsevier Ltd. All rights reserved.O TEXTO COMPLETO DESTE ARTIGO, ESTARÁ DISPONÍVEL À PARTIR DE NOVEMBRO DE 2014.19191

Fuzzy control of a PMMA batch reactor: Development and experimental testing

The present work is concerned with the design and experimental testing of a fuzzy control algorithm for temperature control of a methyl methacrylate (NIMA) batch polymerization reactor. Ethyl acetate is used as solvent and benzoyl peroxide is the reaction initiator. The polymerization reaction is considered a challenge since it presents non-linear and transient behavior. The development of the fuzzy control and its comparison to a conventional PID (velocity form) controller is shown. In the design of PID-fuzzy control, the knowledge obtained from the process reaction curve procedure is employed to determine proper membership functions. Fine tuning is obtained altering the output scaling factor and cardinality. A digital filter was successfully used in order to overcome the oscillatory behavior of the temperature. According to experimental results, the PID-fuzzy was considered more suitable and reliable for this polymerization process control since it outperformed PID velocity form algorithm. (c) 2005 Elsevier Ltd. All rights reserved.30226827

Neural modeling for cytochrome b5 extraction

In this work a neural model for cytochrome b5 extraction in batch and in continuous operation was developed. The best feedforward arquiteture achieved for batch operation modeling was 3-4-1 and 3-8-2 for the continuous operation. It was observed that among the models developed, the best adjustment was that obtained with Bayesian regularization algorithm training. Deviations of less than 10% were observed for the experimental data and they are similar for the neural model, since it was statistically proved the null hypothesis in the comparison between the two independent samples (experimental and predicted outputs). (c) 2006 Elsevier Ltd. All rights reserved.4161272127

Control strategies analysis for a batch distillation column with experimental testing

The dynamic nature and the non-linear behaviour of batch distillation equipment pose challenging control system design when products of constant purity are to be recovered. Several alternative column configurations and operating policies have been studied. However, issues related to the on-line operation of such process have not been properly addressed. The present work describes the investigation with experimental verification of computer based control strategies to batch distillation: a programmable adaptive controller (PAC), a self-tuning regulator (STR) and a non-linear model predictive control (MPC). The developed control systems made the conventional batch distillation column more efficient and easy to operate. Experiments performed on the pilot column confirm the simulation results. (C) 2000 Elsevier Science S.A. All rights reserved.39212112

Development of comparative electronic circuit for reduced flow valve control

Development of comparative electronic circuit for reduced flow valve control. Different types of flow control valves are commercially available but most are only adequate for high flow rates. These arc provided with pneumatic devices to reduce explosion risks in industrial plants. Flow-reduced and low pressure valves are small sized, usually driven by analogical signals and very expensive. Current assay developed an on-line computer-aided flow control electronic system made up of common electronic components within a comparative voltage scheme. A subtracted amplifier was employed in the electronic circuit which compares two voltages for rotation direction control and stoppage of speed engine reducer linked to the common valve stem. This low cost system is very efficient and has the advantage of being electronically assembled without any difficulty. Different sized valves were successfully coupled to the electronic circuit-driven reduction engine which provided flexibility to the developed apparatus. Calibration and time delay determination assays are provided, with lowest flow rates ranging between 0.3 and 3.6 mL s(-1).33218518

Reviving traditional blast furnace models with new mathematical approach

This paper describes how traditional analytical blast furnace (BF) models can be revived by the inclusion of new mathematical tools. Combining some fundamental models with new mathematical algorithms can create efficient and simple to use hybrid models. A hybrid model based on artificial neural network (ANN) and its industrial application to the new BF No. 3 at Companhia Siderurgica Nacional (CSN, Volta Redonda, Brazil) was developed, tested and put in use. In BF operation, which is a multivariable complex process subject to oscillations in raw material characteristics, a precise model is essential to adjust charging and blow conditions to match productivity, chemical quality and target costs. A neural model was developed in order to estimate chemical and thermal parameters to feed a first principles model capable of evaluating alternative operation standards. As a consequence, operation efficiency is being enhanced, leading to higher productivity and lower costs.34541041

A fuzzy-split range control system applied to a fermentation process

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)In this study it was proposed the application of a fuzzy-PI controller in tandem with a split range control strategy to regulate the temperature inside a fermentation vat. Simulations were carried out using different configurations of fuzzy controllers and split range combinations for regulatory control. The performance of these control systems were compared using conventional integral of error criteria, the demand of utilities and the control effort. The proposed control system proved able to adequately regulate the temperature in all the tests. Besides, considering a similar ITAE index and using the energetically most efficient split range configuration, fuzzy-PI controller provided a reduction of approximately 84.5% in the control effort and of 6.75% in total demand of utilities by comparison to a conventional PI controller. (C) 2013 Elsevier Ltd. All rights reserved.142475482Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Identification and predictive control of a FCC unit using a MIMO neural model

The main aim of this work is to implement and evaluate the performance of a neural network-based model predictive control (MPC) applied to a fluid catalytic cracking (FCC) unit. The studies were carried out by dynamic simulation of a Kellogg Orthoflow F converter. The output signals were modified by random noise. From steady-state conditions, a sequence of step changes was imposed on the usual manipulated variables. Information on the process dynamics and interactions among variables is supplied by recording the responses of controlled variables. During the network training procedure, this information was readily captured by the neural model. The neural model output is composed of the four controlled variables, predicted one step ahead. Tests with unseen data showed relative errors of the output variables around 1%. This reliable neural model was then introduced into an MPC scheme, subject to process constraints. Two regulatory and a servo-regulatory problems were simulated. Both the predictions from the neural model and the optimal control calculations could be calculated rapidly, since the control horizon equals 1 or 2. Overall, simulation experiments have confirmed good regulatory and tracking properties of the proposed control system. Simulation tests with noisy measurements provide confidence that the neural model and the controller could be used in an industrial environment. (c) 2004 Elsevier B.V. All rights reserved.44885586

Saving energy using fuzzy control applied to a chiller: an experimental study

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)In this study the energy consumption of a chiller system, under different configurations and control strategies, was investigated. Two control strategies (Proportional integral-PI and fuzzy-PI) were applied to regulate the temperature of propylene glycol solution and the evaporating temperature using compressor motor supply frequency and pump rotation frequency as manipulated variables. The experiments were initiated with the system in open loop. When the steady-state condition was reached the system was submitted to a positive disturbance in its heat load and the feedback control was activated. Hence, the results were analyzed by means of integral of the time-weighted absolute error (ITAE) criterion and total energy consumption. The analysis of experimental results showed that both controllers were able to regulate the controlled variables appropriately. Also, it was verified that the use of the compressor motor supply frequency as manipulated variable can smooth out process variable oscillations. Besides, comparing the energy demands of both controllers, it was observed that the fuzzy-PI controller required 3.4% less electric energy (0.81 kWh). Considering the propylene glycol solution temperature (secondary fluid) as controlled variable and compressor motor supply frequency as manipulated variable, fuzzy-PI control achieves smaller ITAE index value, and energy consumption than the PI counterpart, consuming 0.25 kWh less energy. Thus, energy savings could be attained for control configurations that exhibit higher ITAE values. It was demonstrated that is desirable to include the energy consumption as a control performance parameter in order to increase the energetic efficiency of process.144535542Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP