CFD Modelling for Control of a Chemical Waste Rotary Kiln Incinerator

Copyright'"' IFAC Automation in Mining. Mineral and Metal Processing. Tokyo. Japan. 2001

CFD MODELLING FOR CONTROL OF A CHEMICAL WASTE ROTARY KILN INCINERATOR YongIiang Yang and Marm. A. Reuter

Section ofRaw Materials Technology Department ofApplied Earth Sciences Delft University of Technology Mijnbouwstraat 120,2628 RX De/ft, The Netherlands

Abstract: Chemical waste processing forms part of a complex network for materials processing. The rotary kiln for the incineration of chemical waste is a poorly defined reactor in this network, and should ideally be controlled in a feedforward manner rather than in a feedback manner as is in the practice. However, the process dynamics and the poorly defined nature of the feed (including poorly sampled chemical waste in drums) bring significant challenges in the feedforward control of such high temperature furnaces. To optimally control such systems with very poorly defined parameters, a well-structured database should be built to assist in the control. This concept has been applied to the case of the rotary kiln incinerator through computational fluid-dynamics (CFD) modelling as an off-line tool. in view of process control. The integration ofCFD modelling into the process control system, generation of the CFD database and linking the CFD predictions to the actual process control situation, are discussed in the paper. The database generated serves as direct or indirect support to the operator in making kiln-operating decisions and to improve the process control. Copyright C 2001 IFAC Keywords: feedforwad control, computational fluid dynamics, database structures, rotary kiln incinerator

process control heavily depends on thermocouple measurements taken after the kiln exit plane, because the inside of the kiln cannot be accessed This means that the incineration process can only be controlled in a feedback manner. Therefore, it is difficult or even impossible to anticipate great variations, which results in poor process control. poor conversion of CO to COo destruction of dioxin, etc. For a poorly defined system, it would ideally have to be controlled in a feedforward manner to ensure that complete destruction of the chemical waste is achieved at the same time ensuring that the reactor main1aim its integrity.

1. INTRODUCTION Rotary kilns are widely used in high temperature processing of materials, e.g. Waelz kiln for processing of zinc containing materials, partial direct reduction of laterite and chromite ores, calcination of limestone and production of cement clinker. to name but a few applications. Rotary kilns are also used in hazaIdous waste-incintntion, due to its versatility and flexibility, as illus1rated in Figure 1. The rotary kiln incinerator is like metallurgical furnaces a poorly defined system. In general, there is a lack of information about the chemical waste input and a great variability of the waste composition due to poor sampling possibilities. The

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can assist in the process control of the chemical waste incineration kiln. If simulation results are introduced in the process control in an appropriate way, this will provide a means that would ultimately enable feedforward control of the incineration process. The use of CFD modelling has great potential to improve the process control and to support to the operator in making kiln~g decisions. A better control of the incineration process is essential in the following issues: •

maintaining the desired incineration temperature of about 1300"C inside the rotary kiln to ensure complete destruction of the hazardous chemical waste (e.g. dioxin);

•

improving the refractory lifetime, which can be achieved when the temperature can be better controlled and high temperature peaks be avoided;

•

monitoring the residence time at sufficiently high temperatures to ensure that the residence time of the combustion gases after the last injection of combustion air exceeds 2 seconds at llOO"C as dictated by the European Directive on the Incineration of Waste (since June 2000).

•

increasing throughput of low calorific chemical waste and decreasing the throughput of fuel oil and high calorific chemical waste, to improve the economic performance of the incinerator.

Fig. 1. General structure of the kiln and the secondary combustion chamber (SCC). The physical/chemical processes and dynamic behaviour inside the kiln are as complicated as in metallurgical processes, because of poorly defined characteristics of feed materials (different types of chemical wastes in poorly sampled drums). Simulation of the transport phenomena, e.g. through computational fluid-dynamics (CFD) modelling, in such a process becomes a very challenging task. Attempts have been made by many researchers in the past 20 years to build up different types of models to simulate the flow, heat transfer and combustion processes inside the rotary kiln for chemical waste incineration. More information can be found, among many others, in the following references by Jenkins and Molar (1980), Leger et al. (1993), Jakway et al. (1996), Veranth et al. (1997), Wardenier and Van den Bulck (1997), and more currently in authors group (Rakhorst et al., 1999).

It is important to note that, for the continuously changing conditions at field-scale, CFD models cannot be used directly in on-line process control. The computation time for one simulation run is much too long for direct control purpose. Therefore, an important question is how the CFD modelling can be integrated in the process control system and how the CFD predictions can be linked to the actual process control situation. A concept for these issues is the main point in this paper. Here an example is shown on using CFD in simulating a high temperature chemical waste-incineration process operating at AVR-Chemie in The Netherlands, which is very similar to pyrometallmgical furnaces. The concept is well applicable to pyrometallurgical processes. The potentials and challenges as well as its limitations of using such a tool in process control will also be discussed.

CFD is a mathematical modelling tool to simulated fluid flow related processes. The main objectives of CFD modelling were to get understanding of the velocity and temperature distribution, and how the performance of the rotary kiln would be influenced by basic design modifications and operational parameters, such as thermal energy input, leakage air, etc. In none of the studies it was investigated how CFD modelling could be more directly used as a tool in the process control of a chemical waste incinerator, which would be one of the major engineering goals in industry. Consequently, there is no public knowledge in this area.

2. BASIC INFORMATION FROM CFD SIMULATIONS

This poorly defined chemical waste incinerator plant is situated in a complex web of material flow (Vechoef et al., 2(00). In order to optimise this whole system it is obviously imperative that each of the planWunit operations should be optimally controlled so that the system as a whole is nm environmentally optimally. Therefore, the main objective of this paper is to illustrate how the chemical waste incinerator can be optimally controlled. It is shown how CFD modelling

CFD models for computing gas flow and temperature distribution for chemical waste incineration have been established within a framework of a commercial CFD code PHOENICS. The governing partial differential equations for conservation of mass, momentum, and energy in a turbulent flow system were solved simultaneously for the given incinerator.

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Different turbulence models such as the standard k-e, RNG k-e, and Chen-Kim k-e models were tested [Rakhorst et al., 1999], and was found that the standard k-e model is well applicable and this model was used in the current modelling work. For combustion modelling, a global chemical reaction scheme (SCRS) for chemical waste combustion reactions has been included in the models, and reasonable results for temperature distribution have been obtained and validated with certain field-scale temperature measurement data.

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As a result, distributions of velocity, temperature and other useful variables (e.g. fuel mass fraction) are obtained after the simulation. In addition, residence time distribution was also simulated through numerical tracer tests in a solved CFD model. Statistical RTD analysis was made through the obtained integral and differential curves. Furthermore, residence time distribution of fluid particles after entering the furnace was tracked, which gives further information on residence time distribution. Figure 2 illustrates the velocity distribution across a few regions of the incinerator, and a clear view of the complexity of the flow pattern and mixing behaviour could be seen. Figure 3 illustrates the computed temperature and residence time distribution together with the combustion modelling. Figure 4 shows the predicted RTD curves. The mean residence time, dead volume and mixed flow volume could be calculated.

The information regarding the residence-time, temperature distribution is very useful to characterise different feed types once the model is suitably calibrated. This information then can be used to optimise: •

the chemical waste destruction,

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the energy recovery and hence also the off-gas composition,

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the slag composition, designed for different chemical waste types in view of their Iiquidus temperatures and capacity to contain minor elements, and

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the wear of refractories, affected by temperature (spikes especially), slag and minor elements.

3. INTEGRATION OF CFD MODELLING IN THE PROCESS CONTROL SYSTEM The idea for a feedforward control structure for metallurgical furnace has become a well-established concept in the metallurgical industry nowadays. This will be translated in this paper for the rotary kiln incinerator, from which it will become apparent that a well-structured database could form the pivot around which the supervisory control structure should be built (this is in addition to the low level PLC and other low level feedback control loops) (Figure 5).

Fig. 2. Flow pattern inside the rotary kiln and SCC.

Most elements of the control structure interact with each other and databases play an important role. It is essential that a good process information database is available, which contains the chemical waste input data and temperature measurements that can be correlated with chemical waste input data. This information is needed for the simulation of different kiIn-operating conditions. The simulation results should be fed back to a CFD database that can be used by the operating personnel and the supervisory control system.

Fig. 3. Temperature and entry time distribution across load-chute plane of the incinerator.

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Fig. 6. Oftline CFD simulations and continuous model validation with temperature measurements.

Fig. 5. A control structure of a chemical waste incineration facility with a CFD model database. The various process data can then for example be matched with the CFD results to provide the operator with useful process information that matches the poorly defined feed with various operating conditions. Various intelligent process control tools can be used to achieve this (Reuter et al., 1996; Reuter 1999).

Fig. 7. Post-processing of CFD predictions and use of CFD database in the process control system. It is well known that CFD modelling generates a large amount of output data, which is difficult to use directly in process control. Appropriate postprocessing of the results should be carried out to produce useful data-sets in order to be able to link the CFD predictions to the actual process control situation, which is shown in Figure 7.

The supervisory control refers to a situation in which a computer controls the process and the operators and management interact with this supervisory computer. However, in the short to medium term, it may be expected that the operators remain making the determination of most set-points to the controllers for the input of chemical waste. In reality, it may be difficult to have the supervisory control system taking care of all the decision-making due to the nature of the incineration process. Therefore, the line between the supervisory control system and the controllers is a dotted one.

The development of this concept may ultimately result in a specific indication of the optimum or most desirable values for the set-points of the important controllers within the incineration facility. These setpoints may be provided simply as information to the operator, i.e., the supervisory control system may be open loop, after the operator simply accesses the CFD database via the PC in the control room. In this way the CFD database can provide better information than human experiences or guesses. This information can be used to suggest set-points.

At first, the simulation results should be used to relate various parameters and input streams, to predict temperature distribution, residence time, thermal load, etc. The amount of chemical waste input scenarios should then be increased and optimised in order to be able to determine the best operating strategy for a specific set of input streams and to improve kiln performance. After that, the information should be arranged in such a way, that useful incineration control could be performed in a feedforward manner.

4. LINKING CFD PREDICTIONS TO THE AcruAL PROCESS CONTROL SITUATION As above-mentioned. CFD models provide a lot of information in a standard form, which cannot be directly linked to the actual process control situation. Therefore, proper post-processing of the results is required to produce useful data sets, which vectorise various operating conditions, including the effect of various (poorly defined) feed materials. Only these data sets should be stored in the CFD database. Further investigation should be carried out on this topic, but two potential approaches of how this could be done, are presented in the following section.

In Figure 6, the dynamic process of running off-line simulations of different input scenarios on basis of chemical waste input data and the model validation with temperature measurements is illustrated. Especially in early stages of generating CFD simulations, model validation is essential for a continuous development and improvement of the models. The CFD predictions are eventually stored in the CFD database.

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4.1 Averaged temperature and vector approach Every cross section, which is taken from the model geometry in order to show the simulation results, will show to more or less extent a gradient The visual observation of the CFD predictions may be clear then, but for storage in the CFD database, appropriate data sets are required The temperature gradient at specific locations in the incinerator must be quantified. For this purpose one or more cross sections should be taken, which are supposed to give most information about the incineration process, i.e. cross sections that are most useful as support to the operator and to be supplied to the process control system. The average temperature weighted with mass flow rate gives a quantification of the temperature or enthalpy distribution in a cross section and can be supplied to the database. The geometry of the model is built up of many grid cells. The temperature in each cell is assigned a weight associated with the mass flow rate through the cell. In this way the average temperature is given by a weighted linear combination of all cell temperatures in one cross section, as shown in equation (1):

Fig. 8. Example averaged temperature and vector approach. The center of the average temperature can be calculated based on the temperature moment over x and y c0ordinates, as is shown in equations (4) and (5).

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Further processing of the average temperature can be illustrated in Figure 9, where average temperature profile along the main flow direction of the reactor can be constructed. This is normally required for design and operating purposes of industrial furnaces. However, simple average figures are normally used in practice. The data points in Figure 9 also include the x-y information (hidden), which is obtained from equations (4) and (5). Then a very good picture of the kiln thermal behaviour can be obtained.

In this equation T; is the temperature and mi is the mass flow rate in kg/s in the i-th cell, having a total number of n cells in the cross section. T; can be calculated from the specific enthalpy Hi, if it not solved directly. The averaged temperature can then be expressed as follows in equation (2):

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However, the average temperature does not give any information about the location of the different temperatures. A vector approach may be used to point out the location, for example, the hot region in the cross section. The cross section should then be attached to a co-ordinate system.

An example of this idea is given in Figure 8. By means of integration over the whole cross section for the enthalpy and mass flow rate, the centre of enthalpy may be calculated and coupled to a temperature vector. This will make the average temperature a function of the x and y co-ordinates, in addition to a function of the enthalpy and mass flow rate per cell:

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The information of Figures 8 and 9 can be stored in the database and matched to the appropriate operating conditions. This implies that, given certain feed types, the operator can obtain an immediate picture of the distribution characteristics f::; f(H,m,x,y,z) to optimise the various aspects mentioned. Also, this will enable the operator to select the correct mix of feed materials to ensme a "correct" distnbution as presented by Figure 8 is maintained andIor reached. This approach then becomes a soft sensor for the operation.

4.2 Direct temperature approach The incineration process inside the rotary kiln heavily depends on thermocouples that are installed after the kiln exit plane. This means that the process can only be controlled in a feedback manner. Therefore, it is very difficult to anticipate great variations, which results in poor process control.

should be optimal. Due to the complexity of the various material streams it is often very difficult to optimally control the individual reactors. This paper presented an approach how, in a feedforward manner, CFD can be applied to deal with the processing complexity.

ACKNOWLEDGEMENTS Financial support and permission to publish the data of the kiln operation from A VR-Chemie are greatly acknowledged. Special thanks are due to Mr. J. Verwoerd and Mr. A. Quak from AVR-Chemie for their constant interests in and support to this work.

REFERENCES Jenkins, B.G., F.D. Molar (1980). Modelling of heat transfer from a large enclosed flame in a rotary kiln. Transaction of the Institution of Chemical Engineers, 59, pp. 17-25. Jakway, A.L., A.M. Sterling, V.A. Cundy, C.A. Cook (1996). Three-dimensional numerical modelling of a field-scale rotary kiln incinerator. EnvironmentDl Science & Technology, 30, pp. 1699-1712. Leger, C.B., V.A. Cundy, AM. Sterling (1993). A three dimensional detailed numerical model of a field-scale rotary kiln incinerator. Environmental Science & Technology, 27, pp. 677-690. Rakhorst, J., Y. Yang. M.A. R.euter, J.H.L. Voncken (1999). Computational modelling of the gas flow, mixing, and temperature distnbution in a rotary kiln hazardous waste incinerator. The Phoenics Journal, 12(3), pp. 278-292. Reuter, M.A. et al. (1996). Intelligent control of submerged-arc furnaces for ferroalloys, Journal ofMetals, 48(12), pp. SI-53. Reuter, M.A. (1999). The simulation of industrial ecosystems. Minerals Engineering, 11, pp. 891917. Wardenier, K. and E. Van den Bulck (1997). Steadyslate waste combustion and air flow optimisation in a field scale rotaIy ~ Environmental Engineering Science. 14(1), pp. 43-54. Veranth, I .M., GD. Silcox, D .W. Pershing (1997). Numerical modelling of the temperature distribution in a commercial hazardous waste slagging rotary kiln. Environmental Science & Technology. 31, pp. 234-253. Verhoef, E.V., G.P.J. Dijkema and M.A. Reuter (2000). The complexity of material cycle simulation and design - co-incineration. In: Proceedings MINPREX 2000, 1nl congress of mineral processing and extractive metallurgy, publication series 512000, (p. Griffiths and A. Spry EeL). pp. 553-558. Melbourne, Australia.

The CFD predictions, that give the temperature in the location where normally a thermocouple records the temperature, could replace these measurements. In this way it is possible to a priori have information about potential temperatures that may be reached with different kiIn-operating scenarios. This implies that the CFD results in the database act as a soft sensor, and provide information useful for process optimisation and control.

5. CONCLUDING REMARKS

In this paper, a concept was proposed for using CFD simulation results in a supervisory control system through a rotary chemical waste-incinerator. It is highlighted that a feedforward control is highly needed for such poorly defined furnace systems. To provide the data for the feedforward control system, distnbuted information from CFD simulations after proper processing could be very useful for building up part of the control database. Presently the combustion models for the incineration system are under further refinement, and at the same time, different operating and feed material scenarios are being simulated, and a comprehensive CFD database will be constructed and tested at AVRChemie in their hazardous chemical waste incineration process. This is matched with temperatme measurements to validate the results. The various distnbutions provide the operator with different valuable diagnostic information, which can be used to optimise the plant operation. The complexity of the interaction between various chemical waste and material processing facilities becomes apparent If this complex system is to be optimised, the control on the individual plant level

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