Intelligent sensor for coal powder rate injection in a slag cleaning furnace

Minerals EngJneermg, Vol. 13, No. 7, pp. 777-781, 2000 Pergamon

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TECHNICAL NOTE INTELLIGENT SENSOR FOR COAL POWDER RATE INJECTION IN A SLAG CLEANING FURNACE*

L. G. B E R G H § , J. B. Y I A N A T O S § a n d P. B. C H A C A N A t Chemical Engineering Department, Santa Maria University, Valparaiso, Chile E-mail lbergh~pqui.utfsm.cl t Enginee:ring Department, Caletones Smelter, E1 Teniente, Codelco-Chile, Rancagua, Chile (Received 9 November 1999; accepted 28 April 2000) ABSTRACT An intelligent sensor for estimating the coal powder rate injected into a slag-cleaning furnace (SCF) was developed at Caletones Smelter, Chile. At present the estimation algorithms are only based on discharged cycle average information, leading to large prediction errors. In this paper an improved estimation of the current coal rate is presented, based on the instant tank weights, the tank pressure and the position of the control valves, among other variables. Better control of coal addition into the batch process increases copper recovery from slags and reduces the particulate material pollution. An hybrid expert and fuzzy supervisory control is proposed © 2000 Published by Elsevier Science Ltd. All rights reserved Keywords Artificial intelligence; modelling; pyrometallurgy process control; process instrumentation INTRODUCTION In general, the performance indices of a process can be improved on-line by the appropriate use of the information about the relevant operating variables. In this paper the problem is focused in obtaining the best estimation of a key variable by inference, since no direct measurement is available. One approach, successfully nsed in a number of processes, is to develop a model based on accepted theories and experimental evidence (for example, Isermann (1984)). However, this approach demands accurate measurements and plant models. In mineral processing plants, and particularly in smelters, there are only a few sets of available measurements and most of them of questionable quality. In these cases, inference models or soft sensors should be derived from contaminated measurements and discrete events responding rather to heuristic: relationships. CALETONES SMELTER

Caletones Smeltex is located at the Andes Cordillera, 42 km from Rancagna, at 1556 m over sea level, in Chile. The smelter is part of E1 Teniente Division of Codelco-Chile and produces 380,000 tons of copper, smelting 1,250,000 tons of dried concentrate per year. Nearly 70 % of the slag (8% Cu) from TCs is processed in three Slag Cleaning Furnaces (SCF). In the SCF two products are obtained: the matte (70 % copper) that is returned to the CTs or PSCs furnaces, and the final slag (0.85 % copper) that is dumped in the slag deposit. * Presented at Mi~erals Engineering 99, Falmouth, Cornwall, UK, September 1999

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The slag cleaning technology, under license from Codelco-Chile, Fundicion Caletones Patent (1988), allows recovery of copper contained in high copper grade slag (4 - 10 % copper), derived from smelting and converting processes. The copper is mainly mechanically entrapped as sulphide compounds. The aim of the process is to create favourable conditions to form two main phases: the matte, rich in copper, and the final slag, poor in copper. The slag cleaning technology is a batch process consisting of the reduction of magnetite (Fe304) by adding a reductor agent. In Caletones, powder coal, pneumatically transported by air, is injected into the smelted slag bath (at approximately 1,250 °C) in the furnace. The batch furnace operation is completed in four stages: (i) charge of smelted slag from TC, (ii) reduction of magnetite contained in the slag by adding coal, (iii) sedimentation of the matte forming two phases, and (iv) extraction of the final slag and high grade matte. The furnace is a cylinder of steel, 4.6 m diameter and 12.7 m long, internally covered with refractory bricks, of the basculant type. The SCF is a batch process, where 150 tons of slag is charged initially and 600 kg of coal powder is continuously fed in each furnace cycle. The burner is continuously operated during the complete process to keep the bath temperature close to 1,250 °C.

Coal injection system The coke is transported from the silos to the furnace by using two pressurised vessels alternately. Each day, six cycles of coal injection are performed in each furnace. When one vessel is discharging its content into the furnace, the other is completing its charge from the silos. A simplified diagram of the injection of coal to the furnace process is shown in Figure 1. Each vessel is supported at three points. At each point a strain gage is mounted to measure the vessel weight. The average of these three measurements is communicated to the monitoring and distributed control system (DCS). Each strain gage is periodically calibrated by using weight patrons.

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Intelligent sensor for coal powder rate injection in slag cleaning furnace

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During the vessel discharge, the operator tries to maintain the coke flowrate by manipulating the set point of the pressure controller on the nitrogen line. Sometimes the blow valve is used to reverse the coke compactness in the lower part of the cone. To optimise this technology and consolidate this process, the following improvements have been considered, Achttrra et al. (1997): (i) change to a continuous operation of the process, (ii) minimize the contaminants from gases, and (iii) increase the life expectancy of refractory bricks. To achieve these objectives, the coal rate injection must be controlled. Currently, poor control of the rate of injection may lead to excessive loss of coal and the production of undesirable carbon monoxide in the gases. Too low a rate of injection, however, sloWs down the reaction kinetics and inadequate copper recovery may be achieved. Presently, the copper content in the final slag varies from 0.4% to more than 1.4%, where the copper content is over 1% in 28 % of the cases. This variability is due to many factors, among them the magnetite content in the TC slag and the irregular dossification of coal into the slag furnace. Coal rate estimation

Since weight signals were not reliable, the rate of injection of coal calculated by the difference in weight over a known period of time produces a considerable error, and a large variability of the copper recovery and some other practical problems were observed. Figure 2 (a) shows a typical curve for these estimations. 100 80 60 --ffi E

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Fig.2 Coal ratv estimation and evaluation. The first approachL consisted of filtering the derivative of the average weight signal from the DCS. The filter also consider the I;~sition of solenoid valves and an empirically tuned filter block, as is schematically shown in Figure 2 Co). The prediction results are shown in Figure 2 (c). Since there is no direct measurement of the coal rate, the consistence of the soft sensor has to be checked in term of the cumulative weight over a cycle. Figure 2

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(d) shows the cumulative weight obtained directly from weight measurements from DCS and that derived from the integration of the soft sensor over time. It can be seen that the soft sensor, in the light line, shows a consistent evolution and that it perfectly matches the total coal added to the furnace, appropriately correcting the peaks, shown by a dark line, that have no physical meaning. Since these results were obtained based on a set of data corresponding to twenty hours of operation, the soft sensor was applied to other sets of independent data, showing good results, Chacana (1999). Alter checking the prediction adequacy, the next step was to implement the soft sensor on-line in the DCS. At present the soft sensor provides a continuous estimation of coal rate inject into the furnace, and is used by operators to regulate the pressure on the nitrogen line to modify the rate whenever it is found necessary. The projeci to automatically control the furnaces is based on an hybrid strategy, that is schematically shown in Figure 3. The manipulated variable is the change in the transport pressure under a fuzzy supervisory control. The rules to be used relate the output variable with independent input variables such as the weight and the pressure in each vessel, the pressure in the transport line, and, of course, the information provided for the soft sensor to predict coal rate. The control strategy will be based on expert and fuzzy supervisory control approach, which was shown to be very successfully applied to other mineral processes (Bergh et al., 1998).

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CONCLUSIONS A new soft sensor has been developed based on the average of weight measurements exhibiting frequent faulty signals. Since no adequate models were available, the use of heuristic filters on faulty measurements has proven to be a powerful tool to obtain reliable estimations. The soft sensor has been implemented and is presently used by operators as a guide to operate coal injection into the slag furnaces. This experience will be essential in developing, the operating rules to be implemented in an expert-fuzzy supervisory control. The impact of these development is very important in large smelters, since the reduction of the copper content in the final slag by only 0.1% would increase the revenues by nearly a million dollars per year.

ACKNOWLEDGEMENTS

The authors would like to thank Conicyt (Project Fondecyt 1990859) and University of Santa Mafia (Project 992723) for their financial support. We also are in debt to Division El Teniente, Codelco-Chile where the experimental work has been done and for giving permission to publish the results.

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REFERENCES

Achurra G., Warezok A. et al., Optimizaci6n de Limpieza de Escoria, Internal report, Project CODELCO ENAMI - U. DE CHILE, 1997, Santiago, Chile. Bergh L.G., Yianatos J.B. and Leiva C.A., Fuzzy Supervisory Control of Flotation Columns, Minerals Engineering, 1998, (11), 8, 739-748. Chacana P.A., Detecci6n Inteligente de Fallas en Sistemas de Medieiones: Aplieaei6n a Inyecei6n de Carboncillo en Homo Limpieza Escoria Teniente, Master Thesis, Chemical Engineering Department, Santa Maria University, 1999, Valparaiso, Chile. Fundiei6n Caletones, Division E1 Teniente, Codelco-Chile, Procedimiento Pirometalfirgic'o para Recuperar Cobre en Homo Basculante con Inyeeei6n Sumergida de un Agente Reduetor (Tratamiento de Eseoria), Patent N ° 35.877, February 24, 1988. Iserrnann R., Process Fault Detection Based on Modelling and Estimation Methods: A Survey, Automatiea, 1984, (20), 4, :387-404.

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