Hasil Pencarian  ::  Simpan CSV :: Kembali

Hasil Pencarian

Ditemukan 71974 dokumen yang sesuai dengan query
cover
Rindang Persada
Pemantauan proses produksi gula kristal putih dengan menggunakan multivariate statistical process control = Monitoring of white crystal sugar production process using multivariate statistical process control / Rindang Persada
"ABSTRAK
Saat ini, kualitas telah menjadi salah satu faktor terpenting untuk memenangkan persaingan global. Untuk menghasilkan produk berkualitas, produsen perlu menerapkan pemantauan proses yang baik. Oleh karena itu, perlu adanya metode pemantauan dan pengendalian proses untuk menjamin kualitas produk. Berkenaan dengan banyak variabel yang menentukan kualitas produk gula kristal putih, perspektif multivariat lebih tepat digunakan daripada univariat untuk menghindari inefisiensi dan kesimpulan yang salah. Dalam penelitian ini, diagram kontrol Hotelling T2 digunakan untuk memantau proses dengan banyak variabel secara simultan. Untuk mengidentifikasi variabel yang menyebabkan proses yang tidak terkontrol, dekomposisi Mason-Young-Tracy MYT digunakan. Akhirnya, diagram sebab akibat dan failure mode and effect analysis digunakan untuk mengidentifikasi faktor-faktor potensial yang menyebabkan proses tidak terkontrol.
ABSTRACT
Nowadays, quality has become one of the most important factors to winning global competition. To produce quality products, manufacturers need to implement good process control. Therefore, there needs to be a method of monitoring and controlling the process to ensure product quality. With regard to the many variables that determine the quality of white crystal sugar products, a multivariate perspective is more appropriate to use than univariate in order to avoid inefficiencies and incorrect conclusions. In this study, Hotelling T2 control charts are used to monitor the process with many variables simultaneously. In order to identify which variables are causing uncontrolled processes, Mason Young Tracy MYT decomposition can be used. Finally, cause and effect diagrams and failure mode and effect analysis FMEA are used to identify potential factors that cause uncontrolled processes. "
2018
T50769
UI - Tesis Membership  Universitas Indonesia Library
cover
Mason,Robert L., 1946-
Multivariate statistical process control with industrial applications
"This applied, self-contained text provides detailed coverage of the practical aspects of multivariate statistical process control (MVSPC)based on the application of Hotelling's T2 statistic. MVSPC is the application of multivariate statistical techniques to improve the quality and productivity of an industrial process. The authors, leading researchers in this area who have developed major software for this type of charting procedure, provide valuable insight into the T2 statistic. Intentionally including only a minimal amount of theory, they lead readers through the construction and monitoring phases of the T2 control statistic using numerous industrial examples taken primarily from the chemical and power industries. These examples are applied to the construction of historical data sets to serve as a point of reference for the control procedure and are also applied to the monitoring phase, where emphasis is placed on signal location and interpretation in terms of the process variables."
Philadelphia : Society for Industrial and Applied Mathematics, 2002
e20442739
eBooks  Universitas Indonesia Library
cover
Keller, Paul
Statistical process control demystified
New York: McGraw-Hill, 2011
658.562 KEL s
Buku Teks SO  Universitas Indonesia Library
cover
Statistical process control in manufacturing
New York : Marcel Dekker, 1991
658.5 STA
Buku Teks SO  Universitas Indonesia Library
cover
Badavas, Paul C.
Real-time statistical process control
Englewood Cliffs, NJ: Prentice-Hall, [Date of publication not identified]
658.562 BAD r
Buku Teks SO  Universitas Indonesia Library
cover
Sultan Shiddiqi Salman
Peningkatan Kinerja Pengendalian Proses produksi Formaldehida di PT X Menggunakan Model Predidictive Control = Formaldehyde production Process Control Performance Improvement in Company X Using Model Prdeictive Control
"ABSTRAK
Formaldehida merupakan senyawa kimia yang populer dengan banyak kegunaan, dengan jumlah kebutuhan yang cenderung terus bertambah. PT X merupakan salah satu produsen formaldehida yang masih memiliki permasalahan terkait kapasitas produksinya. PT X masih menggunakan pengendali Proportional-Integral (PI) yang masih mempunyai ruang untuk peningkatan produksinya. Model Predictive Control (MPC) digunakan untuk mengoptimalisasikan parameter pengendalian proses produksi formaldehida di PT X. Model empiris dibuat untuk diterapkan pada pengendali MPC berdasarkan Process Reaction Curve (PRC) dengan menggunakan pendekatan First Order Plus Dead Time (FOPDT). Kinerja pengendali diuji menggunakan set point (SP) tracking dan disturbance rejection. Ada empat pengendali yang diuji, yaitu pengendali laju alir steam (FIC-102), pengendali temperatur udara (TIC-101), pengendali level evaporator (LIC-101), dan pengendali tekanan evaporator (PIC-101). Didapatkan hasil model empirik FOPDT untuk masing-masing pengendali, dengan nilai parameter pengendalian Prediction Horizon (P), Control Horizon (M), dan Sampling Time (T) yang optimal secara berurutan: (1, 2, dan 1) pada FIC-102, (62, 21, dan 1) pada TIC-101, (50, 10, dan 6) pada PIC-101, dan (70, 21, dan13) untuk LIC-101. Terjadi perbaikan kinerja berdasarkan uji perubahan nilai set point baik dihitung melalui IAE maupun ISE sebesar 26,9% dan 8,03% untuk FIC-102, 15,37% dan 32,51% untuk TIC-101, 13,37% dan 25,9% pada PIC-101, serta 23,35% dan 6,71% pada LIC-101. Pada uji disturbance rejection juga terjadi perbaikan kinerja baik dihitung melalui IAE maupun ISE sebesar 96,4% dam 99.74% untuk FIC-102, 13,37% dan 25,9% untuk TIC-101, 54,25% dan 76,67% pada PIC-101, serta 15,96% dan 4,4% pada LIC-101.
ABSTRACT
Formaldehyde is a chemical compound known for its many uses, with the increase of its demand. PT X is one of the producers of formaldehyde that has problems related to its production capacity. PT X right now still uses Proportional-Integral (PI) that still have rooms of improvements. Model Predictive Control (MPC) is used to optimize the process control parameters of formaldehyde production in PT X. The empirical model is made for the MPC based on the Process Reaction Curve (PRC) using First Order Plus Dead Time (FOPDT). The control performance is tested using set point (SP) tracking and disturbance rejection. There are four controls that were tested, which are steam flow control (FIC-102), air temperature control (TIC-101), evaporator level control (LIC-101), and evaporator pressure control (PIC-101). Thus, the results of the empirical FOPDT model for each control is obtained, with the value of Prediction Horizon (P), Control Horizon (M), and Sampling Time (T) parameters are optimal and its value respectively are: (1, 2, and 1) for FIC-102 , (62, 21, and 1) for TIC-101, (50, 10, and 6) for PIC-101, and (70, 21, and 13) for LIC-101. The performance improvement based on the set point change test calculated through the IAE and ISE are 26.9% and 8.03% for FIC-102, 15.37% and 32.51% for TIC-101, 13.37% and 25, 9% for PIC-101, and 23.35% and 6.71% for LIC-101. Based on the disturbance rejection test it is also improvements on the performance both calculated through the IAE and ISE of 96.4% and 99.74% for FIC-102, 13.37% and 25.9% for TIC-101, 54.25% and 76.67% for PIC-101, and 15,96% and 4.4% on the LIC-101."
Depok: Fakultas Teknik Universitas Indonesia, 2020
S-Pdf
UI - Skripsi Membership  Universitas Indonesia Library
cover
Muhammad Zulfikar Fauzi
Peningkatan Kinerja Pengendalian Proses Produksi Formaldehida di PT X Menggunakan Multivariable Model Predictive Control = Improvement of Formaldehyde Production Process Control in PT X using Multivariable Model Predictive Control
"ABSTRAK
Formaldehida merupakan senyawa kimia yang digunakan pada industri perekat. PT X merupakan produsen formaldehida di Jawa Timur. Sistem pengendali proses yang digunakan di PT X masih berbasis proportional integral (PI). Pengendali konvensional ini masih memiliki kekurangan. Multivariable model predictive control (MMPC) diajukan untuk meningkatkan kinerja sistem pengendali pada PT X. Model empiris dibuat menggunakan process reaction curve (PRC) dan perhitungan parameter first order plus dead time (FOPDT). Empat manipulated variable (MV) dan empat controlled variable (CV) membentuk 16 model empiris. Perhitungan parameter MMPC, meliputi sample time (T), prediction horizon (P), control horizon (M), dilakukan dengan metode Shridhar dan Cooper (1998) dan dioptimalkan dengan metode fine tuning. Kinerja pengendalian MMPC diuji dengan perubahan set point (SP) dan ketahanan atas gangguan (disturbance rejection). Empat pengendali yang diuji, yaitu pengendali tekanan evaporator (PIC-101), pengendali liquid percent level evaporator (LIC-101), pengendali laju alir steam (FIC-102), dan pengendali suhu udara (TIC-101). Nilai parameter MMPC meliputi T, P, dan M yang optimal berturut turut adalah 3, 62, dan 2. Pengendali MMPC dapat memberikan peningkatan kinerja pengendalian pada uji SP tracking dengan rata rata sebesar 33,24% untuk IAE dan 42,93% untuk ISE. Sedangkan, pada uji disturbance rejection, terdapat peningkatan kinerja dengan rata-rata sebesar 33,48% untuk IAE dan 58,08% untuk ISE.
ABSTRACT
Formaldehyde is chemical substances that is used in adhesive industry. PT X is formaldehyde producer in East Java. PT X is using proportional integral based control system. This conventional controller has several weaknesses. Multivariable model predictive control (MMPC) is used to increase the performance of control system at PT X. Empirical model is made with process reaction curve (PRC) followed by first order plus dead time (FOPDT) calculation. Four manipulated variable (MV) and four controlled variable (CV) will construct 16 empirical models. Calculation of MMPC parameter, which include sample time (T), prediction horizon (P), and control horizon (M), is done with Shridhar and Cooper method (1998) and optimized by fine tuning method. Performance of MMPC is tested by set point changes and disturbance rejection. Four controllers tested are evaporator pressure control (PIC-101), liquid percent level control (LIC-101), steam flow control (FIC-102), and air temperature control (TIC-101). The optimized parameter of MMPC which include T, P, and M are 3, 62, and 2 respectively. MMPC Controller can increase controller performance in SP tracking with average number of 33.24% for IAE and 42.93% of ISE. Meanwhile, in disturbance rejection, there is an increase in average of 33.485 for IAE and 58.08% for ISE."
Depok: Fakultas Teknik Universitas Indonesia, 2020
S-pdf
UI - Skripsi Membership  Universitas Indonesia Library
cover
Evans, James Robert, 1950-
Statistical process control for quality improvement : a training guide to learning SPC
Englewood Cliffs, N.J.: Prentice-Hall, 1991
658.562 EVA s
Buku Teks SO  Universitas Indonesia Library
cover
Muhammad Adjisetya
Penyetelan Multivariable Model Predictive Control (MMPC) Pengendalian Proses Kompresi Nonlinear Produksi Biohidrogen Menggunakan Metode Turnamen = Tuning of Multivariable Model Predictive Control (MMPC) Control of Nonlinear Compression Process of Biohydrogen Production Using Tournament Method
"Hidrogen merupakan salah satu gas yang memiliki banyak kegunaan. Salah satunya pada industri kimia. Pada pabrik biohidrogen, unit kompresor merupakan salah satu unit yang penting dalam pabrik biohidrogen dari biomassa. Kompresor berfungsi untuk mencapai tekanan tinggi pada kondisi operasi selanjutnya. Multivariable model predictive control (MMPC) digunakan untuk mengendalikan proses pada pabrik. Untuk mendapatkan pengendalian yang optimal, perlu dilakukan penyetelan. Penyetelan akan dilakukan pada Matlab-Simulink yang diintegrasikan dengan Aspen Plus Dynamics. Sistem pengendalian akan dibuat pada Simulink dan simulasi proses akan dilakukan pada Aspen Plus Dynamic. Penyetelan ini dilakukan dungeon metode Genetic Algorithm dungeon metode pencarian seleksi turnamen. Setelah itu, hasil penyetelan akan dijalankan juga dengan unisim design agar kinerja pengendalian dapat dibandingkan dengan penelitian sebelumnya. Model first order plus dead time (FOPDT) digunakan sebagai model prediksi MMPC. Pada penelitian ini, model FOPDT yang digunakan di MMPC pada Matlab harus dihasilkan dengan cara satuan tekanan keluaran kompresor terlebih dahulu diubah menjadi satuan persentase karena MMPC pada Matlab akan menginterpretasikan variabel-variabel perhitungan dalam satuan persen. Parameter time sampling (T), prediction horizon (P), dan control horizon (M) terbaik yang diperoleh dari metode penyetelan seleksi turnamen pada simulasi dengan unisim untuk perubahan set-point (SP) yaitu 1 detik, 18, dan 3. Untuk uji gangguan parameter T, P, dan M yang diperoleh dengan penyetelan fine tuning terbaik yaitu 1 detik, 341, dan 121. Pada simulasi Matlab-Simulink-Aspen Plus Dynamics, parameter T, P, dan M yang terbaik yaitu 0,05 detik, 18, dan 2 untuk perubahan SP dan 0,05 detik, 7, dan 1 untuk perubahan gangguan.
Hydrogen is one of the gases that has many uses, including in the chemical industry. In a biohydrogen plant, the compressor unit is one of the important units in the biomass-based biohydrogen plant. The compressor unit works to achieve high pressure for further operational conditions. Multivariable Model Predictive Control (MMPC) is used to control the processes in the plant. To obtain optimal control performance, tuning process is necessary. The tuning process will be conducted in Matlab-Simulink integrated with Aspen Plus Dynamics. The control system will be designed in Simulink, and the process simulation will be executed in Aspen Plus Dynamics. The tuning was done using the Genetic Algorithm with tournament selection search method. Subsequently, the tuning results will also be implemented in Unisim Design to compare the control performance with previous research. The First Order Plus Dead Time (FOPDT) model is applied as the prediction model for MMPC. In this study, the FOPDT model used in MMPC in Matlab must be generated by converting the compressor output pressure unit into a percentage unit due to the MMPC in Matlab will interpret the calculation variables in percent units. For the set-point change, the best time sampling (T), prediction horizon (P), and control horizon (M) parameters that were obtained from the tournament selection tuning method in the simulation with Unisim design are 1 second, 18, and 3. For disturbance testinwere obtainedest parameters are 1 second, 341, and 121 that obtained by fine-tuning method. In the Matlab-Simulink-Aspen Plus Dynamics simulation, the best parameters T, P, and M for set-point changes are 0.05 seconds, 18, and 2, and for disturbance changes are 0.05 seconds, 7, and 1."
Depok: Fakultas Teknik Universitas Indonesia, 2023
T-pdf
UI - Tesis Membership  Universitas Indonesia Library
cover
Iwan Suparjan
Menghitung kapabilitas proses produksi menggunakan statistical process control
Depok: Universitas Indonesia, 1999
S37400
UI - Skripsi Membership  Universitas Indonesia Library
<<   1 2 3 4 5 6 7 8 9 10   >>