In recent years, the ideas of evolution, complexity, intelligence and life ..... Huang S.H., Statistical modelling for the prediction and control of PCDDs and PCDFs ...
A Nature-inspired-Modeling-Optimization-Control system applied to a waste incinerator plant M. Annunziato, I. Bertini, A. Pannicelli and S. Pizzuti ENEA – Energy New technologies and Environment Agency ’Casaccia’ R.C., Via Anguillarese 301, 00060 Rome, Italy Phone: +390630484411, Fax: +390630484811 email:{mauro.annunziato,ilaria.bertini,alessandro.pannicelli,stefano.pizzuti}@casaccia.enea.it
ABSTRACT: The extensive use of energy generation processes presents a severe challenge to the environment and makes indispensable to focus the research on the maximization of the energy efficiency and minimization of environmental impact like NOx and CO emissions. Therefore, in this paper we report our experience of a Natureinspired-Modelling-Optimisation-Control (NiMOC) system, applied to a MSWI (Municipal Solid Waste Incinerator) plant, which combines fuzzy logic, neural networks and evolutionary algorithms. The main idea is based on the development of an artificial nature-inspired environment which lives in parallel with the process and that synchronously communicates with it in order to dynamically control and optimise it. The whole system has been first experimented on simulator and then on a real MSWI plant (at Ferrara, Italy). Experimental results have shown a performance plant improvement, however there are still open problems, mainly related to modelling, which are discussed. KEYWORDS: on-line control, evolutionary dynamic optimisation, neural modelling, fuzzy performance definition
INTRODUCTION In problems regarding the control and optimisation of complex energy process [2] the not-adaptive approaches are not effective to solve the problem over time. The uncontrolled variables, the process ageing, the unforeseeable effects caused by human errors, the evolution of the process, in most cases require the change of the basic model, of the objectives, or even of the whole process management strategy. A continuous parametric adaptation is necessary but very often is not sufficient, and the ability of the system to change its internal structure is needed. In short, we need information structures able to evolve in parallel to the process we are dealing with. In this context, a typical complex energy process is that of municipal solid waste incinerators (MSWI)[1]. This kind of process burns solid waste with a mixed and variable composition, produces water steam to generate electrical power and finally it is often connected with a remote heating network for public buildings. For these energy recovery capabilities, MSWI are considered in a similar value to renewable energy sources. These complex production processes and the strong non-linearity in the process itself, cause classical control strategies to be no longer effective. Therefore, the main problems with respect to the MSWI modelling and optimal control are the continuously changing fuel composition, the high non-linearity of the process, the lack of important information (unknown disturbances), sensors noise, plant aging, unpredictable critical faults and conflicting objectives (maximization of energy efficiency and minimization of environmental impact). Moreover, approaches based on off-line models [17] are effective only when we are able to measure all the variables influencing the process. This is a situation which in real cases hardly happens. As a result, for such complex processes it is needed a new approach allowing information structures able to evolve in parallel to the process we are dealing with. At present, a promising approach to manage MSWI is that one using computational intelligence techniques in modelling ([4], [8],[9]) and control ([10], [11],[15],[16]), but we are still pretty far from properly handling the problem. In recent years, the ideas of evolution, complexity, intelligence and life reproduction [13] have long been stimulating the collective thinking and nature-inspired scientific approaches (like artificial life [14]) are coming up on the formation of hypothesis and practices to answer to these basic questions. Research and development on nature-inspired algorithms are providing new tools and ideas for the solution of complex problems requiring evolving structures.
THE METHODOLOGY In this paper a Nature-inspired-Modelling-Optimisation-Control (NiMOC) system, combining fuzzy logic, neural networks and evolutionary algorithms, for on-line optimisation and control of a MSWI plant is presented [7]. The content here discussed concern the main results of the European Project NNE5-2001-000141: “Development of Evolutionary Control Technology for Sustainable Thermal Processes” (ECOTHERM). The main idea is based on the development of an artificial nature-inspired environment which lives in parallel with the process and that synchronously communicates with it in order to dynamically control and optimise it. The system continuously gets measurements from the process and provides the process back with the control actions. The main blocks of the proposed NiMOC architecture are the optimisation environment, the performance measurement and the performance estimation (modelling) module (Figure 1:). The main component is the optimisation one. It is an artificial life (ALIFE) environment composed by individuals able to find the optimal solutions through the rules of reproduction and natural selection. The other two provide a value which represents a judgement of quality (performance) of the current/next states of the plant. On the basis of such an information, the evolutionary environment selects the best solution (that is the individual whose genotype represents the best set of regulations) for the current state in order to drive the process toward the predicted optimal condition. Evolutionary Control
Process / Process Plant / Plant
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Figure 1: Evolutionary control approach scheme
THE OPTIMISATION MODULE The optimisation module is an artificial life (ALIFE) environment based on the genetic evolution of artificial individuals, which observe the consequences on the plant performances of the control actions carried out from the operators or by the optimisation system itself. This continuous learning allows adaptation to time cycles (daily, weekly, seasonal) and to aging or modifications of the plant.
Competition (selection) Process measures (Dynamic measures) Performance
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Figure 2: ALIFE environment The use of an evolutionary approach is connected with non-stationary processes, missing information and hardness to build effective models. The basic features of the methodology we propose are:
• no intensive pre-modelling (progressive training directly from the measurements) • following of the process evolution The basic concept is to have an artificial environment living in parallel to the process and that asynchronously communicate with it, in order to dynamically optimise it. We suppose to always measure from the process its current regulations and a quality index called process performance in the following. In the more general approach the information about the optimal operation point produced by the evolutionary optimiser is supplied to a control system which should manage the regulations in order to reach the optimal point identified by the evolutionary optimiser [18]. This control system is not simple to obtain in case of MSWI process because of the difficulty to model the process [17]. On the other hand, the evolutionary approach gives some limited information about process modelling. For this reason our approach has to include some aspects of control directly in the optimisation system. The ALIFE environment derives from the Artificial Society approach [3]. This approach has been tested for the optimisation of a static wellknown problem, the Travelling Salesman Problem, in which it has reached the optimal value for the 30, 50 and 75 town. Recently it has been compared with several other approaches (classical and evolutionary) for neural network training giving very promising results [5]. The ALIFE context is a two-dimensional lattice divided in nxn cells initially empty at the beginning of the evolution. In the metaphor of the artificial life, this lattice represents a flat physical space where the artificial individuals can move around. During the single iteration (life cycle) all the living individuals move in the space, eventually interact with other individuals, and eventually reproduce generating another individual in haploid reproduction. Every m life cycles (m = 10-1000) we apply an access to the process data acquisition to acquire new data (measurement cycle) and compute the current process performance. At every cycle of measurement, a new individual is built including in its structure measures, current regulations and current performance. Finally we insert this new individual in the environment with a starting value of energy. Three blocks compose the data structure of the individual. The first one includes a collection of behavioural parameters regarding dynamics, reproduction and interaction. These parameters don't change during the individual’s life. The second block includes a series of parameters related to the process to control: the regulation and measurement values; these variables don't change during the individual’s life too. The last block includes dynamics parameters (position, direction, curvature), age, energy and performance value. These parameters may change during the individual’s life, due to the current behaviour of the individual or to the process evolutions. The performance is continuously updated using an external problem-specific model. This is due to the possible changes in the unknown variables of the process not represented in the genotype.
Reproduction, Interaction and Selection. An haploid reproduction model has been implemented for the individual reproduction; self-reproduction can occur only if the individual has enough energy and owing to a positive probabilistic test. The genotype of the child individual corresponds to a probabilistic-random mutation of the parent’s solution (regulation set points) in relation to a mutation average rate and mutation maximum amplitude. The application of the mutation mechanism on the genotype can change radically the individual quality and it can change substantially the optimisation strategy over time and situations. When the system is far from the optimum, high values for mutation amplitude are necessary to speed up the environment to recovery the performance. When the system is close to the optimal point, low values are necessary to locate the optimal maximum. For this reason we adopt adaptive value of the mutation amplitude with respect to the performance value. During a reproduction, we mutate the regulations, therefore we don't know the real performance of the child. For this reason we need the performance estimation model to associate the estimated performance to the new individual. When two individuals collide each other, a fight occurs. The winner is the individual characterized by a greater value of performance. The loser individual transfers a part of its energy to the winner, which becomes stronger and increases the probability to meet other individuals and to fight again pushing selection mechanisms of the best individuals in terms of performances. If an individual reduces its energy under a threshold it cannot reproduce anymore. If it reaches the null energy it is considered dead and removed by the environment. Finally, at every measurement cycle the individual who has the best performance is selected and the corresponding solution is suggested as the optimal current solution for the process operation. THE PERFORMANCE MEASUREMENT The second module is the plant performance measurement. It is aimed to provide the evolutionary controller with a global index of the performance, which is used to carry out the individual’s selection. Such a value is defined out of the control context and it constitutes an immediate and powerful instrument to globally monitor the good operation of plant providing a global index in the range (0,1). The definition of this index is obtained by means a problem specific multiobjective approach and it represents the global formalization of the goals we want to fulfil. In this way, in order to combine conflicting constraints and objectives into a unique performance index, we defined a proper combination of fuzzy sets on real measurements. Therefore, for each process variable influencing the effectiveness of the incinerator
management, it has been defined a fuzzy set and a membership function (Table I:) which has been calibrated over the specific process and experimented on real data coming from plant. Index F1 F2 F3 F4 F5 F6 F7 F8
Variable Fuzzy set Membership function Steam “steam optimal” Gaussian Oxygen (O2) “oxygen in working range” Gaussian Combustion chamber temperature “temperature in working Gaussian range” Post-Combustion chamber “temperature in working Gaussian temperature range” NOx Emissions “NOx low” Gaussian CO Emission “CO low” Gaussian Flue gases “low flue gases” Sigmoidal Pressure in combustion chamber “pressure low” Sigmoidal Table I:. Basic fuzzy sets
The main idea driving the definition of the fitness criterion is that of having a flexible function capable to manage different criteria. In particular the fitness function will be the composition of two fuzzy sets describing two different requirements: optimality and penalty. The difference between the two term lies in the composition of the previously defined fuzzy sets. The first one is intended to describe a general evaluation of the performance giving each variable a different weight (importance). This fuzzy set is not meant to check the constraints, if one variable is out of range then it will not severely affect this evaluation. Therefore, the membership function is defined as the weighted sum of the membership functions representing the process production we want maximize. Logically this operator represents a composition standing between AND/OR. O = X1⊕X2⊕….XN (1) µO(x1,x2..xN) = ∑wiµi (xi) ; µO(x1,x2..xN) ∈ℜ, µO(x1,x2..xN) ∈[0,1]; wi ∈ℜ, wi∈[0,1], ∑wi=1 The penalty fuzzy set concerns the approach of conditions close to the constraints violation or critical for the process maintenance or safety or critical for pollutant emissions. Therefore, it is aimed to strictly satisfy all constraints. If one variable is out of range then it will severely affect this evaluation The resulting fuzzy set will be logically defined as the AND composition of the basic fuzzy sets. P = X1∧X2∧….XN (2) µP(x1,x2..xN) = ∏µi(xi), µP(x1,x2..xN) = MIN(µi(xi)); µ P(x1,x2..xN) ∈ℜ, µ P(x1,x2..xN) ∈[0,1] In the case of the application over real MSWI Italian plant (Ferrara) fuzzy sets O (1) and P (2) are defined, according to the basic fuzzy sets of table 1, as : O = F1 (3) P = F2∧F3∧F4∧ F5∧F6∧F7∧F8
(4)
The final fuzzy set describing the global fitness is the weighted difference of the last two fuzzy sets O and P. Weights will be defined by the developer according to the custom requirements depending on the relative importance of the two criteria. F=O⊕P (5) µF(x1,x2..xN) = wµO(x1,x2..xN) + (1-w)(µP(x1,x2..xN) ) µF(x1,x2..xN) ∈ℜ, µF(x1,x2..xN) ∈[0,1]; w ∈ℜ, w∈[0,1] In the next figure we show how fuzzy sets O, P and F behave when applied to real data and we can see how it is itself an immediate and powerful instrument to globally monitor the good operation of the plant.
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PERFORMANCE ESTIMATION AND MODELLING The last important module is the modelling one because, in order to get the optimal control actions, we need to properly predict the behaviour of the variables which determine the performance. Such modelling task is very tough and we tried tackling it comparing two approaches. The first one is based on a table (performance map) continuously updated by the real measurements and the second one is based on neural networks. In the second case we used and carried out different kinds of neural architectures and training algorithms. The performance map has a twofold task. On one hand it is a long-term memory of the control system; on the other hand, it allows the continuous updating of the reference model and so it lets the control system itself to evolve in parallel with the process. So it roughly represents an internal knowledge of the real system. The process of updating of the performance map is rather simple. When there is a new measurement, as we said above, this is inserted in the table in the cell which the discretization of the parameter set refers to. This happens regardless of the previous filling of that particular cell. With this implementation the control parameters are discretised and the values we obtain are intended to be the performance table indexes. In the figure we show the situation with two control parameters indexing the X and Y axis of the table. We have two possible cases, depending on this discretization of the input parameters (Figure 4:): 1. If the input refers to a cell in which we have already inserted a measure then we will consider that quantity as the estimate of the performance we are looking for; 2. If the input refers to an empty cell we use an interpolation mechanism, according to which if we point to an empty cell, we have to search for an already measured value in the neighbourhood, within a fixed range. measurement Regulations interpolation
Figure 4: performance map model In particular we chose a linear interpolation rule K (d ) ⋅ M f + (1 − K (d )) ⋅ R
(6)
where Mj is the measure found in the neighbourhood of the empty cell, R is a random performance estimation of the point and K(d) is a weight coefficient exponentially decreasing with the distance between Mj and R in the parameters space. However this simple knowledge implementation has several drawbacks. First, the discretization of the map severely affects the performance of the system. Second, it works only with systems with a few control parameters because of memory allocation. Third, the interpolation mechanism assumes the performance behaviour to be locally linear. In order to overcame these drawbacks an approach based on neural networks has been carried out. The proposed neural models take as input the control parameters (primary flow air, secondary flow air, re-circulation air, grids speed) and the object signal dynamics and returns a prediction of one performance variable. We compared the Back-Propagation (BP) and evolutionary algorithms ([5],[6]) to train the networks and results on steam prediction, the most important variable object of the optimisation, were pretty much the same (Table II:).
BP-NN ENN Perf. Map Average Value
Training Testing 0.031 0.042 0.038 0.039 0.039 0.064 0.055 0.050
Table II:. RMSE average steam prediction results
EXPERIMENTAL RESULTS The whole NiMOC system has been experimented [7] first on a simulator [17] developed by TNO (Netherlands) in the context of the Ecotherm EU Project and then on a real MSWI plant (AGEA plant, Ferrara, Italy). We have installed the on-line optimisation system in a real plant. This is a full scale medium size plant (Figure 5:) which supplies electricity and remote heating to the Ferrara town (Italy). It is managed by the AGEA/HERA company which is partner of the Ecotherm project. Because of the lack of automatic control due to the plant complexity, the regulation set points are ordinarily manually managed from the operators.
Figure 5: MSWI plant placed at Ferrara (Italy) The NiMOC system has been tested during the month of December 2005 and it has been compared to the results obtained the month before with human control. In the next table results are shown on the most important variables affecting energy production, pollutant emissions and the overall fuzzy performance index (5).
Manual control (nov. 05) 14.55 143 8.8 15.8 33654 0.58
Steam (t/h) NOx (mg/nmc) CO (mg/nmc) SO2 (mg/nmc) Flue gases (nmc/h) Performance
NiMOC system (dec. 05) 14.95 132 6.3 10.2 30673 0.65
Difference +0.4 (+2.7%) -11 (-7.7%) -2.5 (-28.4%) -5.6 (-35.4%) -2981 (-8.8%) +0.07 (+7%)
Table III:. Experimental results on real MSWI plant (one month average) Moreover, in the following graphs we show the comparison of the behaviour for some of the mentioned variables. 18.00
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Figure 7: Comparison of CO emissions
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Figure 8: comparison of NOx emissions
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Figure 9: comparison of the overall fuzzy PERFORMANCE index (5)
DISCUSSION Experimental results show an improvement (+7%) in the overall plant performance, mainly related to a remarkable reduction of pollutant emissions (NOx, CO, SO2, Flue gases). Steam production (which is the most important parameter for energy production) improvement is very little and this is due to a fundamental problem related to predictive models. Steam modelling turned out to be very tough because it is highly non-linear, dynamic and depends on a large number of variables. In particular the last point is extremely relevant because in the situation we faced several important variables (like waste flow rate and waste composition) were missing. In general the predictive model has resulted useful when the
frequency of external unknown disturbances is low and their changes doesn’t not affect much the model efficiency. In the other situations the main drawback is the low ability to locally adapt to sharp changes in external disturbances like sudden changes in the waste calorific value. Secondly, in this process there are fast (like air inlet) and slow effects (like waste inlet) which mix together in a very complex way. Thirdly, the plant works always in transient regimes and never reaches a stationary state. This is because steam depends on combustion which is a dynamical process with a very high inertia and recent studies [12] demonstrated that there are limits on the prediction accuracy related to high inertia systems. These problems turned out to be very complex and the models we carried out were not accurate enough (Table II:). In this way the optimisation system is induced to commit several errors and steam production didn’t remarkably improve. Lastly, the lack of precision of the predictive models induced a too slow recovery from emergency situations and we had to insert a rule-based model to manage emergencies. However, in most situations the overall NiMOC system worked in a satisfactory way as plant operators reported to us and as can be seen from the reported graphs (Figure 6: to Figure 9:).
CONCLUSION In this paper we reported our experience of a Nature-inspired-Modelling-Optimisation-Control (NiMOC) system, applied to a municipal solid waste incinerator (MSWI) plant, which combines fuzzy logic, neural networks and evolutionary algorithms. The main idea is based on the development of an artificial nature-inspired environment which lives in parallel with the process and that synchronously communicates with it in order to dynamically control and optimise it. The system continuously gets measurements from the process and provides the process back with the control actions. The main blocks of the proposed NiMOC architecture are the optimisation environment, the performance measurement block and the performance estimation (modelling) module. The main component is the optimisation one. It is an artificial evolutionary environment composed by individuals able to find the optimal solutions through the rules of reproduction and natural selection. The other two provide a value which represents a judgement of quality (performance) of the current/next states of the plant. On the basis of such an information, the evolutionary environment selects the best solution (that is the individual whose genotype represents the best set of regulations) for the current state in order to drive the process toward the predicted optimal condition. The second module is the plant performance measurement. In order to combine conflicting constraints and objectives into a unique performance index, we defined a proper combination of fuzzy sets on the real measurements. The membership functions of the fuzzy index have been calibrated over the specific process and experimented on real data coming from plant. During experimentation, it has shown its capability to be a useful and immediate tool for the operator to realize the global behaviour of the plant. The last important module is the modelling one because, in order to get the optimal control actions, we need to properly predict the behaviour of the variables which determine the performance. Such modelling task is very tough and we tried tackling it with two approaches. The first one is based on a table (performance map) continuously updated by the real measurements and the second one is based on neural networks. In the second case we used and carried out different kind of neural architectures and training algorithms (Back-Propagation and Evolutionary Algorithms). The whole system has been experimented for one month on a real MSWI plant and a global performance plant improvement, mainly due to a remarkable pollutant emission reduction, has been achieved. However, there are still open problems mainly related to modelling (steam prediction), because we have been not able to carry out a predictive model for steam accurate enough and this did not allow the system to optimise the energy production. This was mainly due to the lack of important information which acted as an external unknown disturbance. In this way, future work will focus on modelling by approaching more complex structures (i.e. fully connected and complex neural architectures), on-line algorithms and dynamical analysis.
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