R. Agustriyanto, Endang Srihari Mochni, P. Setyopratomo
{"title":"间歇式牛奶冷却过程的自回归外生系统辨识","authors":"R. Agustriyanto, Endang Srihari Mochni, P. Setyopratomo","doi":"10.22146/ajche.70546","DOIUrl":null,"url":null,"abstract":"The dynamic model of the milk cooling process from 36°C to 4°C using chilled water available at 2°C has been carried out. The cooling water temperature is kept constant by using a refrigeration unit. The process being studied was a Packo brand milk cooling tank belonging to KUD SAE Pujon (Malang - Indonesia). A fundamental heat balance method was used to derive the model, leading to a first-order transfer function process. For a 2 hours cooling process then, the gain and time constant values are 1.00 and 42.3548 mins respectively, or G(s)=1/(42.3548s+1) (first order process). Deriving system transfer function through a mechanistic model is considered difficult; therefore, in this paper, we explored process identification via Auto Regressive eXogenous (ARX). Transient simulations could then be performed to identify the dynamic behavior of the cooling process. The system was then identified using several orders of the Auto Regressive eXogenous (ARX) model, and then the results were re-tested on different forms of perturbations and obtained quite accurate results. The transfer function identified through the ARX111 is G(s)=1/(42.3729s+1) (first order process), while via ARX441, the 5th order process was obtained: G(s)=(0.02361s^4+0.000371s^3+0.2331s^2+9.27×10^(-7) s+0.0005826)/(s^5+0.03932s^4+9.873s^3+0.2331s^2+0.02468s+0.0005826). These models particularly useful for process control design and analysis.","PeriodicalId":8490,"journal":{"name":"ASEAN Journal of Chemical Engineering","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Auto Regressive eXogenous (ARX) System Identification of Batch Milk Cooling Process\",\"authors\":\"R. Agustriyanto, Endang Srihari Mochni, P. Setyopratomo\",\"doi\":\"10.22146/ajche.70546\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The dynamic model of the milk cooling process from 36°C to 4°C using chilled water available at 2°C has been carried out. The cooling water temperature is kept constant by using a refrigeration unit. The process being studied was a Packo brand milk cooling tank belonging to KUD SAE Pujon (Malang - Indonesia). A fundamental heat balance method was used to derive the model, leading to a first-order transfer function process. For a 2 hours cooling process then, the gain and time constant values are 1.00 and 42.3548 mins respectively, or G(s)=1/(42.3548s+1) (first order process). Deriving system transfer function through a mechanistic model is considered difficult; therefore, in this paper, we explored process identification via Auto Regressive eXogenous (ARX). Transient simulations could then be performed to identify the dynamic behavior of the cooling process. The system was then identified using several orders of the Auto Regressive eXogenous (ARX) model, and then the results were re-tested on different forms of perturbations and obtained quite accurate results. The transfer function identified through the ARX111 is G(s)=1/(42.3729s+1) (first order process), while via ARX441, the 5th order process was obtained: G(s)=(0.02361s^4+0.000371s^3+0.2331s^2+9.27×10^(-7) s+0.0005826)/(s^5+0.03932s^4+9.873s^3+0.2331s^2+0.02468s+0.0005826). These models particularly useful for process control design and analysis.\",\"PeriodicalId\":8490,\"journal\":{\"name\":\"ASEAN Journal of Chemical Engineering\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ASEAN Journal of Chemical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22146/ajche.70546\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Chemical Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ASEAN Journal of Chemical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22146/ajche.70546","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Chemical Engineering","Score":null,"Total":0}
Auto Regressive eXogenous (ARX) System Identification of Batch Milk Cooling Process
The dynamic model of the milk cooling process from 36°C to 4°C using chilled water available at 2°C has been carried out. The cooling water temperature is kept constant by using a refrigeration unit. The process being studied was a Packo brand milk cooling tank belonging to KUD SAE Pujon (Malang - Indonesia). A fundamental heat balance method was used to derive the model, leading to a first-order transfer function process. For a 2 hours cooling process then, the gain and time constant values are 1.00 and 42.3548 mins respectively, or G(s)=1/(42.3548s+1) (first order process). Deriving system transfer function through a mechanistic model is considered difficult; therefore, in this paper, we explored process identification via Auto Regressive eXogenous (ARX). Transient simulations could then be performed to identify the dynamic behavior of the cooling process. The system was then identified using several orders of the Auto Regressive eXogenous (ARX) model, and then the results were re-tested on different forms of perturbations and obtained quite accurate results. The transfer function identified through the ARX111 is G(s)=1/(42.3729s+1) (first order process), while via ARX441, the 5th order process was obtained: G(s)=(0.02361s^4+0.000371s^3+0.2331s^2+9.27×10^(-7) s+0.0005826)/(s^5+0.03932s^4+9.873s^3+0.2331s^2+0.02468s+0.0005826). These models particularly useful for process control design and analysis.