Articles
<![CDATA[Simulation of Water Allocation Optimization Problem ]]>
<![CDATA[Agustriyanto, Rudy]]>
<![CDATA[ EMITTER International Journal of Engineering Technology Vol 6, No 2 (2018) ]]>
Publisher : <![CDATA[Politeknik Elektronika Negeri Surabaya (PENS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (231.878 KB)
|
DOI: 10.24003/emitter.v6i2.244
<![CDATA[Pollution prevention is primarily stimulated by economics, legislation, liability concerns, and the enhanced environmental benefit of managing waste at source. Chemical process industries consume a huge amount of water. Consequently, wastewater streams from such industries which contain various contaminants may create environmental problem. The increasing cost of fresh water supply and wastewater treatment has encouraged process industries to minimize fresh water consumption and waste water generation. This paper presents a formulation of water allocation problem (WAP) in order to minimize fresh water consumption in multi contaminant mass exchanger network. The approach is based on mass balance equation within the system being studied. The problem were then solved by using Matlab Optimization Toolbox.]]>
<![CDATA[DESIGN ASPECT OF NITROBENZENE PRODUCTION ]]>
<![CDATA[Agustriyanto, Rudy]]>
<![CDATA[ Jurnal Neutrino:Jurnal Fisika dan Aplikasinya Vol 11, No 1 (2018): OCTOBER ]]>
Publisher : <![CDATA[Department of Physics, Maulana Malik Ibrahim State Islamic University of Malang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18860/neu.v11i1.5330
<![CDATA[Study on process design of benzene nitrification has been done. The activation energy (Ea) and frequency factor (k0) for this reaction were obtained from the literature. Calculation of physical and chemical properties was conducted automatically in HYSYS using NRTL and UNIQUAC Thermodynamic model. Several aspects of process design were investigated during simulation, i.e., the dependence of reaction conversion to temperature, the effect of sulfuric acid ratio against nitric acid in the synthesis of nitrobenzene in CSTR (Continuous Stirred Tank Reactor), and the effect of reactor series and parallel structure to reaction conversion. The highest conversion that can be achieved in single CSTR amounting to 96.9% at a ratio of sulfuric acid to nitric acid = 3.5, and temperature of 50 ºC. However, based on this study it is recommended that the most favorable condition for the nitration of benzene in an isothermal CSTR is 50 ºC operating temperature and a low ratio of sulfuric acid to nitric acid (i.e., 2.5 – 3).]]>
<![CDATA[Simulation of Continuous Bio-Reactor ]]>
<![CDATA[Rudy Agustriyanto]]>
<![CDATA[ Proceedings of The Annual International Conference, Syiah Kuala University - Life Sciences & Engineering Chapter Vol 5, No 1 (2015): Engineering ]]>
Publisher : <![CDATA[Syiah Kuala University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (102.773 KB)
<![CDATA[Dynamic study of bioprocess system plays a central role in bioprocess control. It is in fact on the basis of the time required for the development of the knowledge process that the total design, analysis and implementation of monitoring and control methods are carried out. Within the framework of bioprocesses, the most natural way to determine the models that will enable the characterization of the process dynamics is to consider the material balance of major components of the process. This article will present simulation results of continuous bio-reactor. The mathematical models for the bio-reactor based on the material balance had been derived (Riggs and Karim, 2006) and would be adopted in this study. Those model were solved and simulated using Matlab. It is found that the dynamic responses of the bio-reactor due to a step change in feedrate are first order.]]>
<![CDATA[Perbandingan Kinerja Penyetelan Hagglund-Astorm dan Tyreus-Luyben pada Sistem Kendali Pendinginan Susu ]]>
<![CDATA[Rudy Agustriyanto]]>;
<![CDATA[Anastasia Febrianto]]>;
<![CDATA[Putu Sri Widiawati]]>
<![CDATA[ Jurnal Rekayasa Proses Vol 12, No 2 (2018) ]]>
Publisher : <![CDATA[Departemen Teknik Kimia Fakultas Teknik Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (413.268 KB)
|
DOI: 10.22146/jrekpros.38211
<![CDATA[ A B S T R A C TMilk cooling process is one of the most important milk processing steps after milk extraction process. Milk cooling process is carried out to inhibit the microbial growth. The milk is cooled from room temperature to 4 °C. Generally, this process is performed in batch installation. In this research, continuous milk cooling would be simulated. First, the model of the system was derived from the mass and heat balance. The simulation results were then identified using the System Identification Toolbox (SIT) on Matlab. System Identification Toolbox was used to build the mathematical models of dynamic system based on measured input and output data. The result of system identification is useful for setting control and stability analysis. The milk cooling system was then controlled by Proportional Integral (PI) Control. There are two kinds of tuning methods that will be analyzed. These are Hagglund-Astorm method and Tyreus-Luyben. The results showed that the control performance tuned using the Tyreus-Luyben method was better than the Hagglund-Astorm method from the criteria of its SSE (sum squared of error).Keywords: control; proportional integral; system identification; tuningA B S T R A KProses pengolahan susu yang sangat penting setelah pemerahan adalah proses pendinginan. Proses pendinginan susu digunakan untuk menghambat perkembangbiakan mikrobia. Susu didinginkan dari suhu kamar sampai 4 °C. Umumnya proses ini dilakukan secara batch pada instalasi di koperasi susu. Dalam penelitian ini, disimulasikan sistem pengendalian suhu susu secara kontinu. Model sistem diperoleh dari penurunan neraca massa dan panas. Hasil simulasi kemudian diidentifikasi dengan menggunakan System Identification Toolbox pada Matlab. System Identification Toolbox digunakan untuk menyusun model matematis dari sistem dinamis berdasarkan data input dan output yang diukur. Hasil identifikasi sistem berguna untuk penyetelan pengendali maupun analisis kestabilan. Sistem pendinginan susu kemudian dikendalikan dengan Proportional Intergral (PI) Control. Metode penyetelan yang dianalisis ada dua macam yaitu metode Hagglund-Astorm dan metode Tyreus-Luyben. Hasil yang diperoleh menunjukkan bahwa kinerja pengendali yang disetel menggunakan metode Tyreus-Luyben lebih baik dibandingkan dengan metode Hagglund-Astorm dilihat dari kriteria SSE nya.Kata kunci: identifikasi sistem; pengendalian; proportional integral; tuning]]>
<![CDATA[METODE OPTIMASI PADA SISTEM PENGENDALIAN PROSES TANGKI PEMANAS BERPENGADUK ]]>
<![CDATA[Rudy Agustriyanto]]>
<![CDATA[ Jurnal Teknik Kimia USU Vol 6, No 3 (2017) ]]>
Publisher : <![CDATA[Universitas Sumatera Utara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (693.268 KB)
|
DOI: 10.32734/jutek.v6i3.18315
<![CDATA[In this research, PID (Proportional Integral Derivative) controller tuning was done using optimization method and the result was compared to the other well-known tuning methods (Direct synthesis, Ziegler– Nichols). The purpose of this research is to obtain the best output response when the set point and disturbance are changed. Tuning is a useful process to set the controller parameter that can affect the output response from the changing set point (servo problem) as well as from the disturbance (regulatory problem). The controller performance can be evaluated from the value of Sum Squares of Error when the system is controlled by PID controllers that is tuned with chosen method. The research result showed that optimization method was better than the other methods because it could give the best result.]]>
<![CDATA[KENDALI SISTEM BIOREAKTOR KONTINYU ]]>
<![CDATA[Rudy Agustriyanto]]>
<![CDATA[ Jurnal Teknik Kimia USU Vol 7, No 1 (2018) ]]>
Publisher : <![CDATA[Universitas Sumatera Utara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (569.43 KB)
|
DOI: 10.32734/jutek.v7i1.20238
<![CDATA[Tujuan dalam industri bioproses umumnya adalah memaksimalkan pertumbuhan mikroba dan / atau produksi beberapa senyawa yang dihasilkan oleh mikroorganisme. Untuk mencapai hal ini dengan cara yang efisien, maka diperlukan untuk menjaga lingkungan yang sesuai untuk mikroorganisme setiap saat. Tujuan utama penelitian ini adalah untuk menerapkan algoritma kendali PI (Proporsional integral) dan PID (Proporsional Integral Derivative) untuk suatu sistem bioproses. Kriteria Bode kemudian diterapkan untuk memeriksa kestabilan sistem diikuti dengan penentuan parameter pengendali menggunakan metode TLC (Tyreus-Luyben). Hasil simulasi lintas tertutup menunjukkan kinerja pengendalian yang dapat diterima.]]>
<![CDATA[Simulation of Water Allocation Optimization Problem ]]>
<![CDATA[Rudy Agustriyanto]]>
<![CDATA[ EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018) ]]>
Publisher : <![CDATA[Politeknik Elektronika Negeri Surabaya (PENS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (231.878 KB)
|
DOI: 10.24003/emitter.v6i2.244
<![CDATA[Pollution prevention is primarily stimulated by economics, legislation, liability concerns, and the enhanced environmental benefit of managing waste at source. Chemical process industries consume a huge amount of water. Consequently, wastewater streams from such industries which contain various contaminants may create environmental problem. The increasing cost of fresh water supply and wastewater treatment has encouraged process industries to minimize fresh water consumption and waste water generation. This paper presents a formulation of water allocation problem (WAP) in order to minimize fresh water consumption in multi contaminant mass exchanger network. The approach is based on mass balance equation within the system being studied. The problem were then solved by using Matlab Optimization Toolbox.]]>
<![CDATA[Perbandingan Kinerja Penyetelan Hagglund-Astorm dan Tyreus-Luyben pada Sistem Kendali Pendinginan Susu ]]>
<![CDATA[Rudy Agustriyanto]]>;
<![CDATA[Anastasia Febrianto]]>;
<![CDATA[Putu Sri Widiawati]]>
<![CDATA[ Jurnal Rekayasa Proses Vol 12, No 2 (2018) ]]>
Publisher : <![CDATA[Departemen Teknik Kimia Fakultas Teknik Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (413.268 KB)
|
DOI: 10.22146/jrekpros.38211
<![CDATA[ A B S T R A C TMilk cooling process is one of the most important milk processing steps after milk extraction process. Milk cooling process is carried out to inhibit the microbial growth. The milk is cooled from room temperature to 4 °C. Generally, this process is performed in batch installation. In this research, continuous milk cooling would be simulated. First, the model of the system was derived from the mass and heat balance. The simulation results were then identified using the System Identification Toolbox (SIT) on Matlab. System Identification Toolbox was used to build the mathematical models of dynamic system based on measured input and output data. The result of system identification is useful for setting control and stability analysis. The milk cooling system was then controlled by Proportional Integral (PI) Control. There are two kinds of tuning methods that will be analyzed. These are Hagglund-Astorm method and Tyreus-Luyben. The results showed that the control performance tuned using the Tyreus-Luyben method was better than the Hagglund-Astorm method from the criteria of its SSE (sum squared of error).Keywords: control; proportional integral; system identification; tuningA B S T R A KProses pengolahan susu yang sangat penting setelah pemerahan adalah proses pendinginan. Proses pendinginan susu digunakan untuk menghambat perkembangbiakan mikrobia. Susu didinginkan dari suhu kamar sampai 4 °C. Umumnya proses ini dilakukan secara batch pada instalasi di koperasi susu. Dalam penelitian ini, disimulasikan sistem pengendalian suhu susu secara kontinu. Model sistem diperoleh dari penurunan neraca massa dan panas. Hasil simulasi kemudian diidentifikasi dengan menggunakan System Identification Toolbox pada Matlab. System Identification Toolbox digunakan untuk menyusun model matematis dari sistem dinamis berdasarkan data input dan output yang diukur. Hasil identifikasi sistem berguna untuk penyetelan pengendali maupun analisis kestabilan. Sistem pendinginan susu kemudian dikendalikan dengan Proportional Intergral (PI) Control. Metode penyetelan yang dianalisis ada dua macam yaitu metode Hagglund-Astorm dan metode Tyreus-Luyben. Hasil yang diperoleh menunjukkan bahwa kinerja pengendali yang disetel menggunakan metode Tyreus-Luyben lebih baik dibandingkan dengan metode Hagglund-Astorm dilihat dari kriteria SSE nya.Kata kunci: identifikasi sistem; pengendalian; proportional integral; tuning]]>
<![CDATA[Worst Case of Relative Disturbance Gain Array for Uncertain Distillation System ]]>
<![CDATA[Agustriyanto, Rudy]]>;
<![CDATA[Zhang, Jie]]>
<![CDATA[ Makara Journal of Technology Vol. 16, No. 2 ]]>
Publisher : <![CDATA[UI Scholars Hub ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[This article discusses the constrained nonlinear optimization formulation for calculating the worst case of lower and upper bounds of relative disturbance gain array (RDGA) for uncertain process models. The proposed approach seeks the minimum and maximum values of the relative disturbance gains subject to the constraints in which the process and disturbance gains are within their uncertainty ranges. RDGA ranges are useful for control structure determination and the related robustness, as they provide information regarding the sensitivity to gain uncertainties. The proposed method is demonstrated by ternary distillation column case study. Closed loop simulation results support the analysis based on the proposed method. It is shown that for a particular degree of uncertainties, the range of process gain determinant should not include zero to ensure the successfulness of the calculation. For the distillation system being studied, the maximum allowable α is 0.339 to avoid the singularity of matrix K.]]>
