Model predictive control design and performance analysis of a pasteurization process plant

Anang,; Hadisupadmo, Sutanto; Leksono, Edi

doi:10.1109/ica.2016.7811480

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…For all the aforementioned benefits, the applications of MPC have been investigated in different sectors such as power electronic converters, aerospace, renewable energy, and food processing. (Vazquez et al, 2014;Anang and Leksono, 2016;Raziei and Jiang, 2016;Zhao et al, 2017). A general block diagram of MPC is shown in Figure 11.…”

Section: Model Predictive Controllermentioning

confidence: 99%

Composite model predictive control for the boost converter and two-phase interleaved boost converter

et al. 2023

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This article compares the conventional model predictive control (MPC) and active disturbance rejection control (ADRC) with a novel MPADRC technique for controlling a non-minimum phase behavior in the DC–DC boost converter. The control of the boost converter is challenging as it is nonlinear, and it shows non-minimum phase behavior in a continuous conduction mode (CCM). Moreover, in this article, the comparison is presented for the boost converter and the two-phase interleaved boost converter using MPC and ADRC, and the effectiveness of the interleaving technique is shown. Finally, it is proved that the interleaving method has much more efficiency and less output ripple than the simple boost converter. To conclude, a novel technique has been introduced that combines both the techniques, that is, MPC and ADRC, in the outer and inner loop with a boost converter, respectively, and the response is clearly the best when compared to the said techniques individually. The overall impact of this technique includes the advantages of both the techniques, that is, the use of MPC allows us to optimize the current value by predicting the future values, and the use of ADRC ensures that the disturbance factor is well tackled and cancels the effect caused by all the disturbances including ignored quantities as well.

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Section: Model Predictive Controllermentioning

confidence: 99%

Composite model predictive control for the boost converter and two-phase interleaved boost converter

et al. 2023

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“…Teknologi proses pasteurisasi dengan menggunakan pengendali Model Predictive Control (MPC) diharapkan dapat menghasilkan keluaran berupa pengendalian suhu susu agar stabil pada target suhu yang diharapkan sebesar 72 ℃ (Anang, Hadisupadmo, & Leksono, 2016;Alamirew, Balaji, & Gabbeye, 2017). Dalam uji coba proses pasteurisasi, model pengendali MPC yang dibangun dengan menggunakan perangkat lunak MATLAB dapat meminimalkan terjadinya lonjakan (overshoot) dengan waktu penetapan (settling time) 712 detik.…”

Section: Pendahuluanunclassified

Pengendali Suhu pada Proses Pasteurisasi Susu dengan Menggunakan Metode PID dan Metode Fuzzy Sugeno

Triwidyastuti

Nizar

Harianto

et al. 2019

JTIIK

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AbstrakProses pasteurisasi berfungsi untuk membunuh bakteri patogen yang dapat mengganggu kesehatan. Selain itu proses pasteurisasi juga bermanfaat untuk memperpanjang masa susu tidak rusak sehingga kualitas susu dapat dipertahankan sampai jangka waktu tertentu. Pada penelitian pengabdian masyarakat ini proses pasteurisasi susu dengan model low temperature long time (LTLT) dibangun dengan menggunakan pengendali PID dan pengendali Fuzzy. Model LTLT dipilih karena adanya kebutuhan masyarakat untuk dapat mencampur susu dengan berbagai perasa selama proses pasteurisasi berlangsung. Tujuan akhir dari penambahan perasa pada susu adalah untuk meningkatkan daya jual dari susu pasteurisasi. Berdasarkan hasil pengujian diperoleh kesimpulan bahwa sistem pengendali PID dengan nilai = 31,8; = 117,8; = 4,3 memberikan respon lebih cepat daripada sistem pengendali Fuzzy berdasarkan pengukuran indikator waktu tunda, waktu naik, waktu puncak dan waktu penetapan. Sebaliknya sistem pengendali Fuzzy menghasilkan nilai mean squared error (MSE) lebih kecil daripada sistem pengendali PID yang menunjukkan bahwa sistem pengendali Fuzzy memiliki fluktuasi kesalahan lebih kecil daripada sistem pengendali PID dalam proses pasteurisasi susu. Akan tetapi, MSE kedua pengendali berada di bawah nilai 1%, hal ini menunjukkan bahwa kedua pengendali dapat mempertahankan suhu susu sesuai dengan rentang suhu standar untuk pasteurisasi susu. Hasil pengujian laboratorium terhadap susu hasil proses pasteurisasi menunjukkan bahwa jumlah cemaran mikroba telah turun pada jumlah sesuai dengan standar SNI pada saat yang sama kualitas susu hasil proses pasteurisasi tetap terjaga. Kata kunci: pasteurisasi, low temperature long time, proportional-integral-derivative, metode fuzzy Sugeno AbstractMilk pasteurization process has benefit for reducing pathogenic bacteria that may harm people's health. At the same time, this process can be used to maintain the milk quality for long period of time. In this research, a milk pasteurization process that based on the low temperature long time (LTLT) was built utilizing the Proportional-Integral-Derivative and the Fuzzy system methods. The LTLT method was chosen in this project due to the need to blend the pasteurized milk with several type of food flavoring to increase the selling power of the pasteurized milk. Therefore, it needs longer pasteurization time. Based on the 30 trials of examination, it showed that the PID controller with values of = 31,8; = 117,8; = 4,3 was able to provide a faster system response time compared to the Fuzzy controller. The measurement was done utilizing several indicators including delay time, rise time, peak time as well as settling time. In contrast, the Fuzzy controller produced a smaller mean squared error (MSE) compared to the PID controller showing that the Fuzzy controller produced smaller error fluctuation in the milk pasteurization process. Nevertheless, the results showed that both controllers exhibited MSE lower than 1%, it indicates that both controllers could maintain milk temper...

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“…MPC presents three major benefits: advance prediction of the model response, constraints handling, and capability to control a multi-input multi-output system. For all these reasons, MPC applications have been investigated in different sectors such as aerospace, renewable energy, food processing and in power electronics converters (Anang and Leksono, 2017; Raziei and Jiang, 2016; Vazquez et al, 2014; Zhao, 2017). A general block diagram of MPC is shown in Figure 4.…”

Section: Mpcmentioning

confidence: 99%

“…The other shortcoming in LQR is constraints handling (Qin and Badgwell, 2003; Zhao et al, 2014). MPC is an efficient and flexible predictive control approach, and is extensively being used in different control purposes (Anang and Leksono, 2017; Belda and Vosmik, 2016; Draganescu, 2015; Judewicz et al, 2016; Wang, 2014). It was initially designed for slow response (i.e.…”

Section: Introductionmentioning

confidence: 99%

Constrained model predictive control for an induction heating load

Nawaz

Saqib

Kashif

et al. 2018

Transactions of the Institute of Measurement and Control

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This paper explores a model predictive control (MPC) strategy with constraints satisfaction for a high power induction heating load. The MPC predicts the state variables and future control sequence of the system in advance and achieves on-line-optimization with a reduced error. The state-space model of the system with a parallel resonant load is developed and then MPC is applied. The proposed approach controls the DC link current at the rectifier output and reactive component of the supply current. The DC current is used to regulate the power of the heating load and the reactive component of input current is kept at zero to attain the unity power factor. The results show that the proposed strategy regulates the power of the heating load, achieves unity power factor at input of the system and handles the variables within the defined constraints effectively.

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Model predictive control design and performance analysis of a pasteurization process plant

Cited by 8 publications

References 4 publications

Composite model predictive control for the boost converter and two-phase interleaved boost converter

Composite model predictive control for the boost converter and two-phase interleaved boost converter

Pengendali Suhu pada Proses Pasteurisasi Susu dengan Menggunakan Metode PID dan Metode Fuzzy Sugeno

Constrained model predictive control for an induction heating load

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