Improving Quality and Profitability with Evaporators and Dryers using Advanced Control Technology

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1 Improving Quality and Profitability with Evaporators and Dryers using Advanced Control Technology D.J. Lovett*, Dr M.E.Mackay* *, Stockport, UK In recent years there has been growing interest in the application of advanced process control strategies to improve foodmanufacturing operations. In fact, for certain processes such as evaporation and spray drying advanced process control (APC) techniques have become well established. APC techniques have been able to increase product yield by driving the process closer to its constraints and taking into account the economics of operation all within the same control algorithm. Adding this to the increased process stability, reduced product variability and improved environmental aspects leads to an impressive argument for using the technology. This article builds on some of the more established techniques, such as model based controllers, and gives an insight into the practical aspects of maintaining optimum performance. (Keywords: Model Predictive Control, Evaporator, Spray Dryer, Sustaining Performance) Introduction Advanced Process Control (APC) applications are in increasing demand worldwide, ref.[6]. As competition increases, companies are seeking new ways to obtain greater throughput & yield from their equipment without necessarily expansion of their production. A "competitive edge" is being sought vigorously in many industries. Probably the overriding factor that has lead to such interest in APC in the ffood industry is the reduced implementation cost. Payback periods of 3-6 months are common, ref. [7], and with continued support services now well established to ensure the up time and agreed benefits are maintained over the life of the system, the risk has been virtually eliminated. Implementation experience in the food industry, and particularly the dairy industry, has demonstrated that the APC installations require more than a basic solution to ensure longevity; they require customisation that is often unique to a site and meets the needs of operational, production, and engineering staff. This paper describes the particular process control issues associated with two of the most common dairy process units; the evaporator and spray drying system. An explanation of the basic process operation and associated control issues are discussed. The paper works through a typical project execution to highlight the practical issues that are encountered in an industrial environment. The project Life Cycle begins with a process survey to obtain a clear understanding of the operational difficulties and the opportunities available for the humble process control engineer. The automation audit is briefly described in this paper, however more details of the breakdown of the pre-project survey and automation audit is described in ref. [9]. If there are sufficient economic benefits the project receives the green light, if not The next section stage of the project, control system design, discusses the APC designs for each individual unit and the specific problems that have been addressed through numerous industrial applications. Included in this sectionthe paper contains a section is reviewa review ofing techniques used to ensure the application sustains the improved performance achieved at commissioning; i.erobustness. robustness and flexibility.; aspects of these semi batch processes that are critical to

2 sustaining the improved performance achieved at commissioning. To finish, we discuss some of the future enhancements that are currently underway to apply new technologies the whole project lifecycle we discuss the maintenance and support that is desirable to reap all the benefits of the installed APC system and, to use an in vogue term, sweat the asset.. Page 2 of 17

3 Process Description In the dairy industry many products are required in powdered or agglomerated (granulated) form to achieve good instant properties. The milk or composite liquid feed must have typically around 95% of the initial water content removed. The feeds are heat sensitive and so the moisture must be removed without causing thermal degradation of the final product. Typically there are three drying stages, evaporation, spray drying and fluidised bed drying. The first and most efficient drying stage is evaporation where around 39% of the evaporative work is done. The second stage, the spray dryer, performs around 57% of the total work and finally the fluidised bed performs the final 4% of the evaporative work. Each process unit is now examined in further detail. Evaporation Evaporation is a key process in the production of a variety of liquid products; in particular milk products, coffee and sugar, to name but a few. It involves heating the feed product and passing this through a series of evaporating columns or effects. A temperature profile across these columns has to be maintained to ensure consistent evaporation and to avoid fouling. Unfortunately, these many stages of evaporation introduce a long transport delay typically of the order of twenty minutes. This time delay coupled with the lively dynamics of the evaporator makes it difficult to achieve automatic control of product density using conventional control equipment. Figure 11: Evaporator Final product density is typically controlled by the process operator, making adjustments to the steam feed to the evaporator steam feed in an attempt to maintain product density close to target. This requires the constant attention of a skilled operative, and whilst the product may be produced within specification for most of the time, there is often room for improvement and financial savings. Process disturbances to the evaporator such as changes in the feed density or steam pressure may cause the product density to become unstable and the skill of the operator is required to settle the plant down to steady state conditions. Start up and changes in throughput are also followed by long periods of unsettled operation before stable conditions prevail. During these transitions the product may be out of specification and have only waste value, or may require re-processing which creates its own problems. Spray & Fluidised Bed Dryers Spray dryers convert liquid feed into powder. Liquid product is pumped into the drying chamber at a high pressure to form a fine spray, hot air is con-currently or countercurrently fed into the chamber. As the fine spray makes contact with the hot air, the moisture is flashed off and small particles are formed which drop to the bottom of the drying chamber. Very rapid evaporation of the water keeps the particle temperature low, thus allowing the use of high inlet temperatures without causing thermal degradation to the product. Fluidised beds are often used in series to provide mild and uniform conditions to prevent deterioration of particle structure, the residence time of the product is longer and the temperature of the drying air is lower than in the spray dryer. Air is passed through a wire mesh belt on a porous plate that supports and conveys the product. The particles are vibrated and when the air velocity is increased to the point where it just exceeds the velocity of free fall (gravity) of the particles, fluidisation occurs. The fluid motion allows each particle Page 3 of 17

4 to make contact with the air and so prevents clusters from forming. wider focus; to understand your the specific process and and company culture, at all levels, thereby generating a solution that is focused on economics and also operational styles, such that the solution will will gain acceptance at all levels. Figure 22: Drying Since the purpose of these schemes is to operate the process at its optimum, which typically means pushing operational constraints, then it comes as know surprise that the scheme is potentially more sensitive to process abnormalities. The task of the control engineer is to ensure that sufficient jacketing is applied to the basic controller that manages process abnormalities. Development of a robust supporting environment is essential for the long-term success of these advanced process control strategies. The following section introduces the basics of MPC applied in the food industry and discusses some of the techniques used develop a robust model predictive controller. The initial performance review should result in a business plan that addresses, as a minimum, the following aspects of your business: Quality Throughput Yield Energy Environment Flexibility Although that s pretty simplistic, it s a good starting point for all small projects. For larger scale projects the investment in development of a Master Plan or similar document is well worth the effort. Once the Master Plan is established the next level of analysis should begin: -The Automation Audit. Process Survey The focus of process control engineers is often to address the aspects of the process that may be improved by the application of new control engineering techniques. Often the goal for the control engineer is to minimise process variances and maximise throughput whilst satisfying other energy and environmental objectives. This is all well and good, however more is needed. Control engineering skills need to be accompanied by consultants with a The Automation Audit Benchmarking your Process: What steps are involved in the benchmarking process and economic benefit estimate? The first stage is to assess the current performance of your process including the existing regulatory control system. This is discussed at length in ref. [9]. The second part of the analysis is concerned with estimating how much headroom is available for you to push your process closer to its upper Page 4 of 17

5 specification limits. This typically involves some form of in depth simulation or either univariate or multivariate statistical analysis.an initial barrier to new technology is usually encountered when it comes to quantifying the potential benefits prior to the actual implementation of a new scheme. By monitoring key performance variables with their associated controllers and applying expert analysis, it is possible to quantify the "missed potential" and consequently lower the risk of investment in new automation projects. It is advisable to test and analyze your process closely and obtain a benchmark of your current plant performance. The benchmark and estimate of potential improvement helps you calculate the project economics and assist in securing management approval for the project. What steps are involved in the benchmarking process and economic benefit estimate? The first stage is to assess the current performance of your process and regulatory control system. There are many tools on the market that help identify the quality of instrument measurements, control loop performance, actuator performance etc. For example the Power Spectrum and the Cross Correlation function of a signal may reveal hidden process oscillations, valve stiction, and poor regulatory tuning. For details of such techniques refer to reference The second part of the analysis is concerned with estimating how much headroom is available for you to push your process closer to its upper specification limits. This typically involves some form of in depth simulation or either univariate or multivariate statistical analysis. Although. In our experience, the statistical analysis is often revealing, however, it tends to be cluttered with numerous discrete events that need to be extracted prior to any calculations of the pperformance and Capability Indicesmetrics. Some engineering judgement is inevitably required in the data analysis process. For example: fluctuations in the process steam supply often limit the ability of the control system to perform; If the steam variations cannot be addressed as part of the particular project under review they need to be included in the analysis as a significant performance constraint. That said, eevents that can be removed by feedforward control, model based control, and other advanced techniques should be retained in the analysis, whilst unmeasured disturbances outside the scope of the controllers need to isolated and accounted for separately. Page 5 of 17

6 This preliminary analysis avoids using the ubiquitous 50% reduction in standard deviation as the basis for performance improvements. The aforementioned analysis highlights when it is unlikely that the control system itself can offer the benefits required to make the project worthwhile, consequently saving significant effort by control engineers and re focusing attention to other areas. Page 6 of 17

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8 The Control Problem u v Model y For both evaporator and sspray dryer the control objective is the same: maximise throughput whilst retaining quality of the recovered product and maximising efficiency., see Figure 3. Figure 34 Model structure. u a vector of independent actuation values applied to the plant (process inputs or manipulated variables); v a vector of measurable external disturbances applied to the plant ; y a vector of measured values monitored from the plant (process outputs or controlled variables). A fundamental parameter of the model is the prediction interval, p, which is the time interval at which the model will predict future values of the output signals. Figure 3: Overview of APC Objectives Theis next section gives a straightforward summary of the process response testing, modelling and controller design. The description avoids mathematical detail, the interested reader is referred to the original work [2] for a full description. Excellent general introductions to process modelling and control can also be found in the references [].All systems discussed in this paper have used the APC toolbox, Connoisseur, marketed by Invensys. For further information please refer to reference 8. Model Identification The model structure used is based on discrete (i.e. sampled) data with multiple plant inputs and multiple plant outputs. The structure is linear, allows for plant input-output interaction, dynamic behaviour and time delays. Figure Figure 43 illustrates the signals associated with the model. The vectors u, v and y represent instantaneous values measured at a particular sample interval. Control Algorithm Connoisseur utilises an empirical model of the process for the purposes of control. The commissioningcommissioning engineer builds the model by performing a series of step tests on the process and executing a least squares fit of the plant data to a model. The Connoisseur controller then uses this empirical model together with the desired tuning weights to form a control cost function. Connoisseur Cost Function Connoisseur minimises this cost function over a design horizon, M, consisting of the weighted setpoint errors e, the weighted delta manipulated variables u and the weighted steady state target errors f, to determine the best control action. Page 8 of 17

9 Controller Design: Evaporator Because of the large time delays in the evaporator, the feedforward variables play a key part in the APC scheme s ability to reduce the standard deviation of concentrate density. Any variation in these variables under a conventional PID control scheme would only be seen when their effects cause a deviation in concentrate density from its setpoint. The conventional controller would then try to correct for the error, however, no manipulated variable can cause a response fast enough to make a difference. By implementing these incoming disturbance variables as feedforward variables in a multivariable control scheme, the variation in the concentrate density can be significantly reduced. Connoisseur offers an APC solution to evaporator control in the form of a multivariable model based predictive controller (MPC). A multivariable empirical cause and effect model of the process is obtained and utilised for the purposes of control. The multivariable model characterises the process interactions and time delays between all the major variables. This permits both feed flow rate and energy to be manipulated to achieve control of all critical variables, compensate for any diminishing heat transfer capability of the evaporator and maintain maximum evaporation capacity. The generic design of evaporator APC control scheme is given in Figure 5.in Figure 4. The outlet density of the concentrate product is maintained at setpoint by co-ordinated manipulation of the MVR Fan speed, TVR steam pressure and the evaporator inlet flow. The control scheme accounts for variations in inlet feed density and temperature using feedforward compensation. The primary actuator constraints constraints on the actuators include Inlet flow ratesetpoint, MVR Fan Speed, TVR steam header pressure setpoint. The constraint limits on these manipulated variables are usually well defined, however the same is not true for other process variables. The process variables that are under control fall into two categories; those that need to be maintained at a specific target value (Setpoint Variables) and those which may vary within defined upper and lower bounds (Soft Constraint variables). In the setpoint category are Ffinal product quality (e.g. concentrate solids) is typically maintained at an operator entered setpoint or within fairly tight operator entered limits. In the constraint category are process tank levels, inter-effect vapour temperatures and flows and steam valve. Using constraint bands frees the manipulated variables (actuators) to be positioned, by an optimiser (discussed later) at a position that minimises the operation cost. Figure 5Figure 4: Evaporator APC Control Scheme The controller maintains the outlet density at setpoint whilst ensuring all other constraints are not violated, e.g. inter-effect vapour temperatures and concentrate flow rates. Controller Design: Dryers The output of a spray dryer may be a final product or the feed to a downstream unit; in either case, tight control of product quality is essential. The two main quality variables are powder moisture and bulk density. The last decade has seen vast improvements in online moisture measurement capabilities, this which provides manufacturers with the opportunity to implement closed loop moisture control. Powder moisture and bulk density are highly correlated, so reduced variation in powder moisture will also give a reduced variation in bulk density. High moisture in the product will give increased yield and reduced energy costs, however wet powder will also cause operational problems such as blockages. For Page 9 of 17

10 this reason, operators will have a tendency to over dry the powder and so reduce profits. Offspec moistures will result in the powder having to be blended, reprocessed or discarded. Offspec bulk density will result in packing problems, under-filled or over-filled boxes. Powder moisture varies with feed flow rate, feed moisture, air temperatures and air flow rates. Tight control of powder moisture requires maintaining an accurate and optimum ratio among these variables while remaining within the defined process constraints. When the process has uses a fluidised beds after the spray dryer, the moisture meter is typically placed after themlocated at the end of the bed, as this provides a close representation of is closer to the final product moisture. Naturally Tthis adds a significant time delay to the process which causes difficulties for conventional PID schemes. Conventional PID control will only have one manipulated variable and will not begin to react until a deviation in the product quality appears, this will give poor, suboptimal control and lead to possible instability. Connoisseur offers aone means of effectively solving this problem is through the use of a multivariable model based predictive control controller. solution. A multivariable empirical model of the process is obtained and utilised for the purposes of control. The multivariable model characterises the process interactions and time delays between all the major variables. This permits both feed flow rate and energy to be manipulated to achieve control of all the critical variables whilst maximising throughput. and optimisation schemes around them. In the spray drying APC scheme, this allows tight control of the dryer outlet temperature to a setpoint chosen by the moisture controller, see Figure 6Figure 5.. Controlling the dryer outlet temperature with fast inner loops allows the MPC controller to react quickly to any dynamic disturbances before they are seen in powder moisture. The slower outer loops manipulate the outlet temperature setpoint and the air flows and temperatures in the fluidised beds to achieve the required mean powder moisture. This design adds to the robustness and integrity of the control system in the presence of moisture meter failure or spurious readings. Connoisseur gives superior control performance by allowing feedforward compensation of variables such as feed variation to be included in the control problem, any upsets that occur in these feedforward variables will be corrected for before they are seen in the powder moisture. One of these constraints deserves further explanation: The outlet air humidity limit is a calculated constraint that is a function of the ambient air humidity, feed rate and outlet temperature. This constraint avoids the product build-up, particularly in the cyclones which are susceptible to plugging. By having this calculated variable included in the control scheme ensures that the differential temperature across the dryer can be maximised whilst still operating a safe distance from the blocking constraint. Optimisation Typically an evaporator and spray dryer combination are used in the food industry to convert liquid feed into a powdered product. This conversion process is energy intensive, constrained by both product temperatures and operating conditions and is subject to irregular disturbances. The particular manner in which the system is operated is production driven, however it is typical to see the spray dryer being the dominant of the two units. Figure 6 Figure :5: Spray Dryer APC Control Scheme Connoisseur offers the flexibility to implement several MPC controllers and construct cascade Page 10 of

11 When all of the optimisation and constraint conditions are considered, it becomes apparent that there is an optimum operating point to drive the plant to. To find the optimum point a suitable algorithm would be a Linear Programming algorithm (LP), whereby a quantity of linear relationships and physical constraints would be constructed to best represent the problem. Often this problem is continually changing during production and would need to be solved online. Figure 6: Overview of Optimisation Objectives Figure 6 demonstrates a real optimisation problem. A variety of process Performance Measurements are plotted against different operating points, in this example each operating point refers to a throughput rate. In order to optimise the running of a process, these performance measurements would typically be maximised or minimised, e.g. Minimise Energy Usage Maximise Feed Rate Maximise Product Quality Maximise Process Efficiency The figure shows how each performance measurement may vary as throughput increases. Energy Usage may remain reasonably linear within the considered region, but also may have a slight exponential increase with throughput. Feed Rate will of course typically be linear with throughput. Product Quality and Efficiency may have more complex relationships with throughput, positive with lower rates until reaching a point of inflexion when the relationship then becomes negative. There will of course be constraints imposed on the optimisation problem, real physical constraints, such as boiler steam supply capability, shown on the plot as energy constraints. There will also be product quality constraints or specification constraints, outside of which, the product is either rework or waste. Prior to constructing thean optimiser it is important to have an understanding of the relative costs that need to be assigned to each of the dependent (process values) and independent (actuators) variables. These decisions are typically straightforward, for example there is a smaller cost assigned to MVR Fan variation compared with TVR variations. Evaporators are significantly more efficient than the spray dryers so consequently an optimiser would be designed to increase the % evaporative strip using the evaporator rather than the dryer, naturally within the constraints defined. The dryer is supplied with feed from a balance tank that acts as a buffer between evaporator and dryer. The balance tank level will need to remain within limits for operational reasons, but otherwise the level may vary in such a way as to allow the evaporator and spray dryer some freedom in throughput; this greatly assists stability. Stability is a key economic factor - maintaining a material balance across the evaporator/dryer combination reduces stoppages of either unit. Stoppages cause, off spec product, lost production time and additional equipment stresses; a good percentage of these are unnecessary. The type of optimisation used in connoisseur relies on a steady state process model. Short-term dynamics on the plant are handled by the individual control systems discussed earlier in this document, using model predictive control and/or other advanced control methods where appropriate. The structure of the steady state model needs to be chosen with care. Mass and energy balances clearly must be satisfied and important economic variables included in the structure. Optimiser Model Predictive Controller Model Predictive Controller Page 11 of T-1 L

12 Fig 7: Overview of the LP Optimiser coupling the evaporator and dryer MPC systems and acting as a Production Manager. As the evaporator is times more efficient than the drying system, the Total Solids from the evaporator should be maximised. By changing the Total Solids from 48% to 50% increases efficiency AND also increases the throughput through the dryer by 9%. This provides significant benefits when production is dryer limited. If the throughput cannot be increased due to evaporator limitations then the optimiser will cut back on the dryer inlet air flow rate, thus reducing the energy usage per kg of product produced. An online optimisation scheme will always be seeking to maximise throughput on an evaporator/dryer process. Having a milk feed density reading available will allow the optimiser to increase the milk feed rate into the evaporator as higher solids in the milk feed appear, as opposed to simply reducing the heat energy in the evaporator. The following section shows some typical APC and Optimisation results achieved on an evaporator and spray dryer. Comments Without APC With APC Using 2 Typical Standard Deviations =95.44% of the time and a realistic 43% 1.33% to 0.76% Reduction through APC %Solids Target can be raised from 50.43% to 51.00% Powder Flow Rate can be increased from 7154kg/hr to 7326kg/hr Milk Feed Rate can be 57381kg/hr to 58761kg/hr increased from Total % Throughput 2.4% Increase Fixed Dryer Evaporative Rate = 6500 kg/hr Fixed Powder %Solids = 96.25% DryerEvapRate. Conc.% Solids PowderRate = Powder% Solids Conc.% Solids Powder% Solids PowderFlow Milk FeedRate = MilkFeed% Solids Figure 7: Calculations demonstrating a 2.4% uplift in Throughput when a realistic 43% reduction in Concentrate %Solids Standard Deviation is achieved. Typical Results Figures 8 and 9 show typical snap shots of key process variables under an APC and Optimisation scheme on an evaporator/spray dryer system. Page 12 of

13 Figure 8: Standard Deviation Curves showing increased mean through APC. If the throughput cannot be increased due to evaporator limitations then the optimiser will cut back on the dryer inlet air flow rate, thus reducing the energy usage per kg of product produced. There is less erratic movement on the Pre- Heat Temperature manipulated variable. Figure 9 shows how APC and LP Optimisation achieves tight control of the Exhaust Temperature whilst maintaining Concentrate Tank Level and maximising throughput. Maximisation of thermal efficiency of the dryer Defined as Efficiency = (T inlet - T outlet )/(T inlet - T ambient ) Therefore maximising the Delta T across the dryer will increase efficiency. Calculation to demonstrate the increase in drying capacity by an increase in Delta T of 5 oc to follow. Fig 9:Spray Dryer control and throughput maximisation Also seeking to exploit the variations in inlet feed solids and push the process to its maximum throughput. Fig 8:Evaporator control and Optimisation Figure 8 shows tyical APC control and LP Optimisation on an evaporator. The Figure shows how superior concentrate density control is achieved, whilst optimising the balance of the TVR and MVR stages of the evaporator. It can be seen that after APC was switched on: - Tight control of Exhaust Temperature is achieved. Concentrate Tank Level is maintained around 50%. Evaporator Feed Rate is maximised. Powder Flow Rate is maximised. These results, however, can not be sustained without significant consideration to creating a robust and friendly environment in which to run. The following section discusses some of the highly important enhanced robustness techniques used in conjunction with APC and Optimisation methods. There is a significant reduction in Concentrate Density variation. The pressure is reduced in the less economical TVR Stage. The speed of the more economical MVR Fan is increased. The Feed Flow Rate is maintained for steady throughput. Page 13 of

14 Techniques used to enhance robustness Our experience has shown a very wide variety of standards in operation of the key process units and an equally wide variation in the type and frequency of unmeasured disturbance capable of disrupting steady operation. The customisation of each system to address the unique operation styles is probably the most important aspect of gaining acceptance in the industry. There is a culture of operation being an art rather than science that needs to be accommodated. Since the purpose of these schemes is to operate the process continuously at its optimum, which typically means pushing operational constraints, then it should come as no surprise that the scheme is potentially more sensitive to process abnormalities. The task of the control engineer is to ensure that sufficient jacketing is applied to the basic controller that manages process abnormalities. Development of a robust supporting environment is essential for the long-term success of these advanced process control strategies. The following section introduces the basics of MPC applied in the food industry and discusses some of the techniques used develop a robust model predictive controller. Naturally the most important benefit to an operator is the ability of the control system to compensate for the regular unmeasured disturbances, e.g. silo changeover, fouling, instrument failure, steam losses, variation in feed properties. These events are frequent and cannot typically be addressed completely; they need to be managed by a thoroughly robust design. This section discusses some of the control techniques that have been used to achieve good results.long term installations. Model or gain scheduling? Each Model Predictive Controller can contain multiple dynamic models of the process. This can be useful, for example where a non-linear process response can be characterised by a number of linear approximations (piece-wise linearisation). Typically, this is used when either significant throughput changes are made or new products are processed. The controller switches dynamically between models while the controller is active, with out the need to reinitialise the new model. On-line checks can also be performed to compare accuracy of a number of models, selecting the model which best describes the process dynamics for control. Setpoint Error Process Measurement Multivariable Model Predictive Controller Fig 10: Automatic model switching occurs when operational conditions changed significantly. Inferential Soft Sensors 01EG40E09.pv01AP01E196Gen.ActivePoweMW One of the advantages of establishing a model of the process is the availability of model estimates of for some of the key process parameters that you seek to control. In the absence of a valid instrument measurement it is possible to rapidly switch over to the inferredthe inferred measurement without loss of control. Process Measurements Model 1 Inferential Model Model 2 High Load Fig 11: Inferential Estimator incorporating Lab Updates When the actual process measurement returns, the system is configured to validate the reading and if OKthen switch back to the measured value. This mechanism proves very effective with the intermittent loss of Density and Moisture measurements Low Load Update 01EG40E09.pv 01AP01E196Gen.Active PoweMW Inferred Variable Page 14 of

15 A sample of results have been collated for this paper to indicate the diversity of schemes that are required to satisfy the needs of the individual clients. Fig 12 shows the robust behaviour during an instrument failure. The density meter signal is registered as BAD which triggers the control system to switch to use the estimated value. As the measurement returns, 20 minutes later, it is validated and then re-used by the controller. Fig.. shows the robust behaviour during an instrument failure. The density meter signal is registered as BAD which triggers the control system to switch to use the estimated value. As the measurement returns it is validated by the controller and re-used by the controller. Achieving a truly robust system relies upon an inherent understanding of all scenarios that will be presented to the controller and predesigning a system that is not ill-conditioned. The first stage to achieving such robustness is by thorough simulation of the system, which, when supported by SVD analysis is able to highlight such mathematical ill-conditioning. However, in practice it is often necessary to continue even when the control problem presented is ill conditioned, simply because the only manipulated variable available are relatively ineffective. So to make appropriate control actions under all operational conditions, the system needs to be able to manage unexpected events and apply prioritisation to selected controlled variables. Achieving appropriate de-selection of (shedding) Controlled Variables in order to square the control problem and focus on the higher priority variables is known as Controlled Variable Ranking and is used to good effect in the evaporator and dryer controllers. For example, under certain constraint conditions the evaporator control of Total Solids is temporarily relinquished when the intermediate flow constraint, i.e. tube coverage, is predicted to be low, thus avoiding severe fouling. Similarly for the dryer; the moisture control is temporarily relinquished as the outlet temperature exceeds its soft constraints. Control focuses on ensuring the outlet temperature remains safely within its operational envelope thus avoiding wetting the dryer. Fig Fig 12: Inferential estimate used as back-up for the Analyser. Future Developments Controlled -Variable Ranking The next stage for development involves collaboration with process manufacturers such as APV. Our intention is to enhance the current APC solution to push the constraint boundaries further using first principle models. Fine tuning the operating envelope (i.e. the Page 15 of

16 constraint boundary within which we operate) is the next stage to achieve true optimisation. Developing a greater understanding of the design constraints will allow the control engineer to sweat the asset further. New areas to investigate and subsequently new constraints to incorporate within the control system may be derived from the following observations ref [5]: Increasing feed temperature causes a reduction in the feed viscocity and consequently increases the dryer capacity by reducing the heat required to raise the droplet temperature inside the dryer. It is difficult to adjust liquid flow rate with pressure nozzles without affecting final powder properties. Higher inlet air temperature results in greater evaporation in the dryer but also increases the humidity and therefore contributes to a decrease in evaporation in the dryer there exists an optimal inlet air temperature. Upper constraint on dryer outlet temperature to avoid thermoplasticity. Enhancements ignition temperature of the powder, thermal degradation and hygroscopicity. Hygroscopicity is interesting Higher inlet air temperature results in greater evaporation in the dryer but also increases the humidity and therefore contributes to a decrease in evaporation in th edryer there exists an optimal inlet air temperature. Upper constraint on dryer outlet temperature to avoid thermoplasticity i.e. Lower constraint on dryer outlet temperature being the moisture in the final product. Consideration of ambient humidity during conditions of dry weather air inlet temperature may be increased and reduced during wet/humid weather conditions. Control of relative humidity of the product. Consider position of the humidity sensor, measurement of dry bulb and wet bulb temperatures. Steam flow rather than pressure control. The next stage for develoment will involve collaboration with process manufacturers such as APV. Our intention is to enhance the current APC solution to include aspect such as ambient humidity compensation, inferential moisture control, and push the constraint boundaries further using first principle models. Our aim is to be able to implement the control solution with as few instruments as practical, relying predominantly on soft sensors. Increasing feed temperature causes a reduction in the feed viscocity and consequently increases the dryer capacity by reducing the heat required to raise the droplet temperature inside the dryer. Consideration of control design for different atomizers: Difficult to adjust liquid flow rate with pressure nozzles without affecting final powder properties. Fine tune the operating envelope (constraint boundary within which we operate e.g. Upper constraint on inlet air temperature related to Page 16 of

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18 Results Economics rough figures from MG Page 18 of

19 Conclusions This paper has described an approach to evaporation and drying that has proved successful on many sites worldwide. It has highlighted that the attainment of both increased product quality and yield are possible using Advanced Process Control techniques without major investment in new plant or instrumentation. Our experience has shown that an essential pre-requisite to a successful project is a wellstructured economic benefit analysis of the system. Assessment of the business objectives alongside the assessment of the existing control system performance ensures correct performance metrics can be established at the onset. Our systems that have been installed worldwide enable the final product and intermediate product variance to be reduced by at least 50% thus allowing the final product moisture /bulk density to beset closer to the maximum specification constraint. This process provides an in built on-line performance monitor available for sustaining benefits. Our results show that coordinatedco-ordinated control of both evaporator and spray dryer improves overall stability, thus leading to less shutdowns, less operator intervention, less plant fouling and increased throughput. The operational envelope of both the evaporator and spray dryer are pushed to a practical optimum operating point each cycle of the controller/optimiser (e.g. every 30 seconds) to ensure production is kept to a maximum and product remains within specification. This would prove to be a very tedious task for operators.. The final product and intermediate product variance are reduced by at least 50% thus allowing the final product moisture /bulk density being set closer to the maximum specification constraint. When designed well with some of the techniques discussed in this paper the model based controller can increase run time by providing greater stability. The improved stability is attained through the effective use of model scheduling, Controlled Variable Ranking, soft sensors and occasionally rule based procedures and soft sensors. From the control engineers perspective, future enhancements to these systems will probably come from the equipment designers sharing some of the design insights and pushing the operational envelop further.temporary Instrument failures are ignored by the controller by immediately switching over to a soft sensor reading. Sustaining the benefits achieved at commissioning require ongoing maintenance of the system using a combination of personnel training, on-line diagnostics and remote support facilities. These activities typically reveal new areas of performance improvement and process optimisation that would otherwise be missed. Acknowledgements References 1. Sandoz, D.J.,Wong,O. & Bloore,C.G. Computer control of the manufacture of spray dried milk powder KEMTEK Conference, Copenhagen, April MPC - SandozSandoz and Wong Design of Hierarchical Computer Control Systems for Industrial Plant, Proc IEE, 1979, Vol 125, no Evaps - Dutton, R A Connoisseur of Predictive control, the Chemical Engineer, 11 Sept, Humidity and Dryer Control, F.G.- Shinskey, Foxboro Internal publication. 5. Performance assessment of control loops: B. Huang & S. Shah 5. Gibson, S., How to optimise your spray Dryers performance Powder & Bulk Engineering, April, Advanced Process Control techniques for the Food Industry: Dept of food science Purdue University (Hyley/Mulvaney) 7. Mackay,M.E., Investigation of the ability of Model Predictive Control to increase Page 19 of

20 powder production capacity at Murray Goulburn s Koroit plant 8. Simsci website located at stm 9. Conneally, Martin and Lovett An econometric approach to justifying advanced process control projects, Article published in Control Engineering Solutions magazine. Page 20 of

21 APPENDIX A Constants and Assumptions Dryer Evaporative Rate 6500 kg/hr Powder %Solids 96.25% Skim Milk %Solids 9.6% Whole Milk %Solids 12.5% Skim Milk Feed Rate 70,602 kg/hr Whole Milk Density kg/lt Skim Milk Density kg/lt DryerEvapRate. Conc.% Solids PowderRate = Eq.1 Powder% Solids Conc.% Solids Powder% Solids PowderRate Milk FeedRate = Eq.2 MilkFeed% Solids Milk % Solids Milk Flowrate = Concentrate % Solids Concentrate Flowrate Eq.3 Concentrat e% Solids Concentrate Flowrate = Powder% Solids Powder Flowrate Eq.4 Increased Solids Setpoint By achieving less variation in the concentrate density through APC, allows the density setpoint to be increased without going outside the product specification, see. This leads to increased throughput and greater efficiency. Financial benefits are only achieved when running dryer limited products, i.e. whole milk products, during peak season. The table below shows how the increase in throughput is calculated. Using 2 Typical Standard Deviations = 95.44% of the time and a realistic 50% Reduction through APC Without APC With APC 2.0 % to 1.0 % %Solids Target can be raised from 49.0 % to 50.0 % Powder Flow Rate can be increased from 6741 kg/h 7027 kg/hr to Milk Feed Rate can be increased from kg/h kg/h Total % Throughput Increase % Figure 9: Calculations demonstrating 4.24% uplift in throughput when a 50% reduction in concentrate %solids standard deviation is achieved. Maintaining Constant/Maximum Evaporation Rate in the Evaporator. Multivariable control and optimisation allows the process to drive towards constant, maximum evaporation by constantly manipulating the feed flow rate. With constantly changing solids in the evaporator feed, optimum performance can only be achieved by having a constantly changing feed flow rate to compensate. Studies show that typical inconsistencies in milk feed leads to a 0.47% increase in milk feed rate through APC. This benefit can be realised during the hectic peak season and is valid for both skim and whole products. SKIM MILK PRODUCTS Without APC With APC Increase in Skim Milk Feed Rate from 70,602 lt./h to 70,935 lt./h.expressed in kg/h 73,088 kg/h to 73,432 kg/h Page 21 of

22 WHOLE MILK PRODUCTS Without APC With APC Increase in Whole Milk Feed Rate from lt./h to lt./h.expressed in kg/h kg/h to kg/h Increased Moisture Setpoint By achieving less variation in the powder moisture through APC, allows the moisture setpoint to be increased without going outside the product specification, see. This leads to an increased powder yield, this benefit is realisable all year round and valid for both whole and skim products. Without APC With APC 2 * PM Standard Deviations Reduced from 0.25 % to % Hence Raise PM Target from 3.75 % to % Decreasing PM Total Solids from % to % % Moisture Increase 3.33 % New Powder Flow due to %PM 7027 kg/hr 7046 kg/hr 50% conc solids % Powder Production Increase = (New PM TS - Old PM TS)/new PM TS 0.27 % Whole Milk Concentrate Powder Yield Flowrate lt/hr Flowrate lt/hr Flowrate lt/hr Without APC % % Powder Increase With APC % 4.86% Skim Milk Concentrate Powder Yield % Powder Flowrate lt/hr Flowrate lt/hr Flowrate lt/hr Increase Without APC 73, % With APC 73, % 0.6% Page 22 of