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Advantages of the self organizing controller for high-pressure sterilization equipments


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A study of a self-organizing controller is implemented in a way that response to controlled system follows the desired given by the model. The self-organizing controller has proven to be a valuable tool in sterilization equipment in order to verify the capacity of the response to any change in the pressure or temperature. Basically, this type of controller is based on the Self-Organizing Map (SOM) that is a neural network algorithm of unsupervised learning. The new ideas include clustering visualization, interactive training and one-dimension arrays.

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Advantages of the self organizing controller for high-pressure sterilization equipments

  1. 1. Practice Article Technical note: Advantages of the self-organizing controller for high-pressure sterilization equipments V. Pilipovik a , C. Riverol b,n a AIChemEng Research and Development Group, J.C Engineers & Partners, Av. Andres Bello, Edif. Las Rozas, Urb. La Florida, Caracas, Venezuela b Chemical Engineering Department, University of the West Indies, St. Augustine Campus, Trinidad, Trinidad and Tobago a r t i c l e i n f o Article history: Received 9 February 2012 Received in revised form 3 July 2013 Accepted 7 July 2013 Available online 29 August 2013 This paper was recommended for publication by Dr. A.B. Rad Keywords: Spores SOM Sterilization Adjustment mechanism Controller a b s t r a c t A study of a self-organizing controller is implemented in a way that response to controlled system follows the desired given by the model. The self-organizing controller has proven to be a valuable tool in sterilization equipment in order to verify the capacity of the response to any change in the pressure or temperature. Basically, this type of controller is based on the Self-Organizing Map (SOM) that is a neural network algorithm of unsupervised learning. The new ideas include clustering visualization, interactive training and one-dimension arrays. & 2013 ISA. Published by Elsevier Ltd. All rights reserved. 1. Introduction High-pressure processing has emerged as an attractive processing technology for preservation of foods. Since the introduction of high pressure processing in 1990 [1,2] the range of products has gradually expanded. The effect that this type of processing has on microorgan- isms is similar to pasteurization while the product retains its fresh or just-prepared appearance and nutritional quality [1,2]. High pressure processing causes minimal changes in the ‘fresh’ characteristics of foods thereby eliminating the processing by thermal degradation. Compared to thermal processing, high pressure sterilization (HPS) results in foods with fresher taste, and better appearance, texture and nutrition. The technology is especially beneficial for heat sensitive products. A recent innovation in sterilization of food products using high pressure is the complete inactivation of vegetative micro-organ- isms, as well as spores, resulting in ambient stable products shown in [2,3]. High pressure sterilization is a combined process where both pressure and temperature contribute to sterilization. A good control system is necessary for keeping the temperature and pressure in the adequate values with less adverse effects on product quality. In this paper a self-organizing controller is introduced as a hierarchical structure in which the inner loop is a table-based controller and the outer loop is the adjustment mechanism. The Self-Organizing Map (SOM) is especially suitable for data analysis [4] where the application in process control is focused on training neural networks [4–6]. It creates a set of prototype vectors representing the data set and carries out a topology preserving projection of the prototypes from the d-dimensional input space onto a low-dimensional grid. This ordered grid can be used as a convenient visualization surface for showing different features of the SOM. In its basic form, it produces a similarity graph of input data. It converts the nonlinear relationships into simple geometric relationships [7,8]. 2. Laboratory set-up and results Destruction of Clostridium sporogenes spores by high pressure treatment was used as the study case. In the laboratory, a sterilizing stainless steel tank of 4 L capacity and 1.5 m length was used, see Fig. 1. The tank has 3 PT100 for monitoring the temperature at the center of the tank. One of the PT100 is located at the top of the tank, another at the middle and the last one at the bottom. The accuracy of the sensor is 70.1 1C. The controller received one value (the average of the three temperatures) because the values from the top to the bottom do not differ more than 1 1C. Contents lists available at ScienceDirect journal homepage: ISA Transactions 0019-0578/$ - see front matter & 2013 ISA. Published by Elsevier Ltd. All rights reserved. n Corresponding author. Tel.: +1 8686622002. E-mail address: (C. Riverol). ISA Transactions 53 (2014) 186–188
  2. 2. The first step is to justify the SOM ability analytically. The first approach was to restrict the consideration to one-dimensional linear unit with which each scalar valued input signal is connected. The SOM control system (SOMCS) adapts the system in accordance with the desired response [7–9]. Each sample is recording and the deviation of desired state is evaluated. An example of the best match is shown in Fig. 2. According to the kinetics of the spore, the adiabatic temperature can increase from 2.2 to 9.3 1C/100 MPa approximately; thus the equipment was tested using three pres- sure pulses of 100, 300 and 600 MPa for 1, 3 and 5 min each, as reported in Table 1. Nevertheless, the importance of the minimum product temperature for the degree of spore inactivation confirms that the temperature should be monitored during high pressure sterilization. On the other hand, the SOM neural network was designed using cluster membership for test data sets that include various levels of data dispersion combined with outliners. A clustering Q means partitioning a data set into a set of clusters Q [10–12]. In crisp clustering, each data sample belongs to exactly one cluster. Fuzzy clustering is a generation of crisp where each sample has different degrees of membership. However, this leaves much room for variation within and between cluster distances and can be defined as follows: dsl ¼ minjjxiÀxjjj ð1Þ dcl ¼ maxjjxiÀxjjj ð2Þ dml ¼ ∑i;jjjxiÀxjjj NiNj ð3Þ outliner ¼ jjciÀcjjj ð4Þ where N is the total value inside the cluster i or j, dsl is the single linkage, dcl is the complete linkage, dml is the average and outliner is the distance between centers shown in [9] and [10]. An example of the cluster definition results is shown in Fig. 2 where the deviation is indicated (d). The idea behind the adapta- tion is to let the adjustment mechanism update the values in the control table F on the basis of the current performance of the controller as reported in Fig. 3. The adjustable neural network taught in previous simulation is inserted in the SOC using the model M. This model was empirically calculated. The adjustable neural network is tuned with each experimental result in the plant (P). Adiabatic heating is the uniform temperature rise within the product, which is solely caused by pressurization [7]. The control Fig. 1. The equipment used in the laboratory. Fig. 2. Cluster definition results. Table 1 Experimental set-up and temperature control (SOC). Pressure (MPa) Target temp (1C) Initial temperature (1C) Mean temperature achieved (1C) SOM Mean temperature achieved (1C) PI 100 70 40 70.8 71.2 85 55 85.6 85.9 100 60 101 99.6 300 70 41 70.1 71.2 85 58 84.9 84.6 100 62 100.6 101.3 600 70 51 71.2 71.6 85 56 85.3 85.9 100 66 100.3 100.7 V. Pilipovik, C. Riverol / ISA Transactions 53 (2014) 186–188 187
  3. 3. system was implemented using MATLAB 7.0, real time toolbox and MF624 multifunction I/O card. A SOM was trained using the sequential training algorithm [8]. All maps were linear. In the agglomerative clustering (see Fig. 2), single, average and complete linkages were used in the construction phase. For additional information about SOM, readers are referred to [5]. In this article the training time was 32 h using 10,000 values collected from 2005 to 2010. The input values were obtained during the last 5 years because this equipment worked 35 years without any control system. How to tune the gains does deserve attention, since they may finally stand in the way for a successful implementation. The SOC works ‘surprisingly well’ [13]. For example, the output gain is lowered between two training sessions. The adjustment is com- pensated by an F-table with numbers of larger magnitude [13–15]. Therefore it is possible to start with a linear F-table, and set the gains loosely according to a PID tuning rule or hand-tuning. This is a good starting point for self-organization. The SOM network can improve the quality of decisions in cluster analysis especially in non-uniform cluster densities [5,6,13]; for example, temperature is a parameter that changes very fast; thus some of empirical data can become “messy data”. Fig. 4 depicts an example in which the SOM controller offers a better performance than the PI controller. Basically, the overshoot is reduced and the setting conditions are attained early. The highest temperature with the SOMCS is about 19.5 1C lower than that with PI as reported in Fig. 4. The same behavior was observed 24 times during the testing sections. The reduction of the overshoot can be translated in a reduction of the steam consump- tion; thus some hundreds of dollars can be saved. Also an over- shoot over 10 1C is not desirable because the food can become dry and lose its texture because of which designing of new controllers may continue in this area. Table 1 indicates that the SOMCS offers a better performance over PI in this system and shows that the results are not sensitive to the initial learning coefficients at different initial conditions. In summary, the new control system shows a good performance and can also improve the traditional PI controller; nevertheless, future research should focus on reducing the training time for the SOM because its implementation on the equipment consumes a long time. 3. Conclusion This paper presented the development of a SOM controller and its application to a high pressure sterilization equipment. The algorithm presented a satisfactory performance and efficiency. The simulation showed that it is possible to achieve a good behavior after few steps of learning using a simple model. The implementa- tion in the sterilization equipment improves the response capacity of the control system although the implementation of the self- organizing system consumes time and is complex. The adjustable control table (F) can be increased using more data for improving the accuracy; however a good computer should be used for reducing the time consumption (the computer used in this article has a 2GB RAM only). References [1] Matser A, Krebbers B, van den Berg R, Bartels P. Advantages of high pressure sterilization on quality of food products. Trends in Food Science & Technology 2004;15(2):79–85. [2] Meyer R, Cooper K, Knorr D, Lelieveld H. High pressure sterilization of foods. Food Technology 2000;54(11):67–72. [3] Hoogland H, De Heij W, Van Schepdael L. High pressure sterilization: novel technology, new products, new opportunities. New Food 2001;3:21–6. [4] Jantzen J. Foundations of fuzzy controller. 1st ed. NY, USA: Wiley; 117–39. [5] Bloch G, Denoeux T. Neural networks for process control and optimization: two industrial applications. ISA Transactions 2003;42(1):39–51. [6] Das S, Saha S, Das S, Gupta A. On the selection of tuning methodology of FOPID controllers for the control of higher order processes. ISA Transactions 2011;50 (3):376–88. [7] Dovžan D, Škrjanc I. Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes. ISA Transactions 2011;50(2):159–69. [8] Lokriti A, Salhi I, Doubabi S, Zidani Y. Induction motor speed drive improve- ment using fuzzy IP-self-tuning controller. A real time implementation. ISA Transactions 2013;52(3):406–17. [9] Tisan A, Cirstea M. SOM neural network design—a new Simulink library based approach targeting FPGA implementation. Mathematics and Computers in Simulation 2013;91:134–49. [10] Rizal M, Ghani Jaharah A, Nuawi M, Hassan C, Haron C. Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system. Applied Soft Computing 2013;13(4):1960–8. [11] Yamazaki T Mamdani TE. On the performance of a rule-based self-organizing controller. In: Proceedings of the IEEE conference on applications of adaptive and multivariable control. 19–21 July, Hull; 1982. p. 121–32. [12] Vesanto J, Himberg J, Alhoniemi E, Parhankangas J.. Self-organizing map in Matlab: the SOM Toolbox. In: Proceedings of the Matlab DSP conference. Espoo, Finland; 1999. p. 35–40. [13] Nauck D, Klawonn F, Kruse R. Foundations of neuro-fuzzy systems. NY, USA: John Wiley & Sons; 37–56. [14] Kohonen T. Self organizing maps. 3rd ed. NY, USA: Springer; 71–99. [15] Ghaseminezhad M, Karami A. A novel self-organizing map (SOM) neural networks discrete gropus of data clustering. Applied Soft Computing 2011;11 (4):3771–8. Fig. 3. SOC control system. The solid line indicates the field data and the dash lines indicate the calculated values. 0 5 10 15 20 25 30 35 40 45 50 0 20 40 60 80 100 120 Time (min) Temperature(oC) PI SOM-NN Fig. 4. Behavior of the temperature using different controllers. P¼600 MPa. V. Pilipovik, C. Riverol / ISA Transactions 53 (2014) 186–188188