Hasil Pencarian  ::  Simpan CSV :: Kembali

"

Penelitian ini menawarkan pengujian dan evaluasi dari pengaplikasian pengendali nonlinear model predictive control (NMPC) konvensional dan economic NMPC (E-NMPC) pada sistem reaktor biokimia dengan laju pertumbuhan monod dan penghambat substrat. Tujuan utama pengendalian dengan NMPC adalah optimisasi teknis yaitu dengan meminimalisir deviasi dari nilai konsentrasi biomassa dalam reaktor dengan nilai yang diinginkan. Selain itu, tujuan utama pengendalian dengan E-NMPC adalah optimisasi ekonomi dengan mengoptimisasi produksi biomassa yang dihasilkan reaktor. Variabel yang dikendalikan (CV) adalah konsentrasi biomassa dalam reaktor, sedangkan variabel yang dimanipulasi (MV) yang juga menjadi variabel keputusan pada komponen optimisasi pengendali adalah laju dilusi. Dilakukan identifikasi sistem serta formulasi algoritma dan optimisasi pengendali E-NMPC. Penyetelan pengendali NMPC dan E-NMPC dilakukan dengan fine tuning terhadap parameter-parameter tuning pengendali. Pengendali yang telah disetel disimulasikan pada perangkat lunak optimisasi paralel dengan fine tuning dari pengendali E-NMPC. Untuk menguji performa pengendali, diberikan gangguan step pada konsentrasi substrat umpan untuk mengamati respon pengendali terhadap gangguan tersebut. Parameter utama yang akan dievaluasi untuk meninjau kinerja pengendali adalah besar fungsi objektif ekonomi. Disamping itu, ditinjau juga profil MV, ISE dari CV, serta waktu komputasi pengendali. Hasil simulasi menunjukkan bahwa skema pengendalian dengan NMPC konvensional mampu menjaga dan mengubah CV ke nilai yang diinginkan. Selain itu, skema pengendalian dengan E-NMPC memiliki produktivitas berupa produksi kumulatif biomassa yang lebih tinggi daripada skema pengendalian dengan NMPC konvensional, namun memiliki waktu komputasi yang jauh lebih lama.

---

This research proposes an examination and evaluation on the application of conventional nonlinear model predictive control (NMPC) and economic NMPC on biochemical reactor system with monod and substrate inhibition growth kinetics. The NMPC controller’s main objective is technical optimization which minimizes the controlled variable deviation from a desired set point, whereas the E-NMPC controller’s main objective is economical optimization which maximizes the cumulative biomass production of the reactor. The controlled variable for this research is the biomass concentration insisde the reactor, whereas the manipulated variable, which also acts as a decision variable for controller optimization, is the dilution rate. Identification of the system is initially done along with formulation of the control algorithm and optimization problem statement for the E-NMPC controller. Tuning of the conventional NMPC and E-NMPC controller is done by fine tuning of the tuning parameters. A step disturbance of feed substrate concentration is used to test the controllers‘ performance. Main evaluation of the controllers‘ performance will be based on economic cost function. Other parameters that will be evaluated are the MV profile, ISE of the CV, and controllers‘ computation time. Result shows that the conventional NMPC schemes are able to bring or maintain the controlled variable to a desired set point. However, the ENMPC scheme outperform the conventional NMPC in cumulative biomass production along the simulation period at the cost of higher computational time.

"

"This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations;Ø Nonlinear systems described by first-principles models and nonlinear systems described by black-box models;- Nonlinear systems with continuous control inputs and nonlinear systems with quantized control inputs;- Nonlinear systems without uncertainty and nonlinear systems with uncertainties (polyhedral description of uncertainty and stochastic description of uncertainty);- Nonlinear systems, consisting of interconnected nonlinear sub-systems.The proposed mp-NLP approaches are illustrated with applications to several case studies, which are taken from diverse areas such as automotive mechatronics, compressor control, combustion plant control, reactor control, pH maintaining system control, cart and spring system control, and diving computers. "

"Kemampuan produksi minyak di Indonesia semakin menurun sejak tahun 1997 hingga sekarang sedangkan kebutuhan produk minyak/ BBM menunjukkan kecenderungan yang semakin meningkat. Maka produk dimetil eter (DME) dapat digunakan sebagai sumber energi alternatif yang lebih ramah lingkungan dan berkelanjutan. Pada pabrik purifikasi DME ini, umpan dengan komposisi DME, metanol dan air akan dipisahkan sehingga diperoleh DME murni dengan konsentrasi 99%. Dalam proses produksinya, unit-unit proses mengalami banyak gangguan yang berdampak pada menurunnya efisiensi dan kestabilan operasi dan juga berpengaruh pada aspek keselamatan.Pada penelitian ini, pengendali Model Predictive Control (MPC) memiliki kinerja yang lebih baik dibanding pengendali PI dalam mengatasi gangguan dengan penurunan integral of absolute error (IAE) sebesar 40,08% hingga 96,26% dari pengendali PI. Parameter penyetelan (tuning) pada pengendali MPC yang berupa sampling time (T), prediction horizon (P), dan control horizon (M) dicari menggunakan metode non-adaptive dan fine tuning. Analisis kelaikan ekonomi pemasangan MPC menunjukkan bahwa payback period adalah sebesar 14,5 tahun dan 13,4 tahun serta net present value (NPV) sebesar -11juta rupiah dan -9,3 juta rupiah pada skenario gangguan umpan 5% dan 8% secara berturut-turut, sehingga penggantian pengendali dari PI menjadi MPC pada pabrik purifikasi DME secara ekonomi tidak menguntungkan.

---

Oil and gas production in Indonesia always decreasing since 1997 until now, and yet the need of oil and fuel product show increasing trajectory. Dimethyl ether (DME) can be used as altenative energy source, it is environmentally safe and sustainable. In this DME purification plant, feed stream containing DME, methanol, and water mixture is separated to obtain DME with 99% purity. In its production process, process unit in DME plant must get disturbances that will affect to the decreasing of process efficiency, operation stability and even safety aspect.

In this research, Model Predictive Control (MPC) has better performance than PI controller in order to overcome disturbances with error (IAE) reduction ranging from 40,08% up to 96,26% than PI controller. Tuning parameters in MPC controller, which are sampling time (T), prediction horizon (P) and control horizon (M), are estimated by both non-adaptive and fine tuning method. Economic feasibility analysis on MPC controller implementation shows that the payback period is 14,5 years and 14,3 years, then NPV -11 million rupiah and -9,3 million rupiah in disturbance scheme of 5% and 8% respectively . Hence, it is not economically feasible to change PI controller into MPC controller on dimethyl ether purification plant."

"In electric power systems that consist of some generators, electric power stability in supplies sidebecomes the most important problems, which must be paid attention. In the interconnection system, ifthere are some troubles in transmission, generator or load will cause another generators feel theexistence of instability condition. For instability condition which not too serious, system can overcomethe fault and will not influence stability of system as a whole. However, for in big scale of fault andhappened in a long duration can be ejected system becoming unstable and will result hampered ofelectrics energy supply to the load For the worst condition could be blackout condition.This article studies about improvement of the stability of the system by using excitation current andthe prime mover of generators, which is coordinated fuzzy logic control in synchronize generator. Byusing annexation from three methods above, the condition of stability of the power system can attain thestability. The transient stability needed control in order that system with good stability can return tonormal condition. Faulted electric power system often caused by failure in controlling the transientstability. It is because in transient stability forms critical condition for electrical power system. By controlling the level of excitation current and mechanical energy from the prime mover ofgenerators which controlled by fuzzy logic when the fault is happened will make acceleration areabecome decreasing and deceleration area become increasing with the result that system can be stablequickly. It visible that from result of simulation obtained if using generator oscillation of fuzzy logiccontrol, transient period becoming shorter and amplitude of oscillation wave is smaller compare by usingwithout fuzzy logic. Likewise, this method is able loo to overcome transient condition at starting period ofa generator."

"Sistem pendinginan pada ruang pusat data menjadi hal yang sangat penting bagi keselamatan sistem komputer di dalamnya Oleh karena itu diperlukan pengendalian yang baik pada Computer Room Air Conditioning CRAC ruang pusat data tersebut Karena itu pada penelitian ini akan digunakan pengendali Model Predictive Control MPC model nonlinier yang mampu menangani sistem multivariabel dengan cukup mudah dan juga kemampuannya untuk memberikan constraint atau batasan tertentu baik pada sinyal pengendali maupun pada keluaran sistem Sistem CRAC merupakan sistem multivariabel berorde tinggi yang memiliki dua masukan dan dua keluaran Model nonlinier sistem CRAC diperoleh dari hasil pemodelan menggunakan persamaan fisika Sementara model linier yang akan dipakai diperoleh dari hasil identifikasi subspace MOESP Multivariable Output Error State Space dan Least Square di mana hasil identifikasi MOESP digunakan dalam pengendalian MPC untuk model linier dan hasil identifikasi Least Square digunakan dalam pengendalian MPC untuk plant nonlinier Hasil pengendalian menggunakan MPC untuk plant nonlinier ini akan dibandingkan dengan pengendalian MPC untuk model linier.

---

Cooling system in data center is become important thing for durability computer system inside Therefore the good controller required for Computer Room Air Conditioning CRAC in data center Because of that in this research will be used Model Predictive Control MPC controller which capable to easily handle multivariable systems and its feature to provide constraints both for control signals and output signals CRAC system is a high order multivariable system with two inputs and two outputs Nonlinear system model of CRAC are obtained from modelling using physics equation Besides that the linear model that is used in the controller are obtained from identification with subspace MOESP Multivariable Output Error State Space and Least Square which the result of MOESP identification will be used in linier model MPC controlling and the result of Least Square identification will be used in MPC controlling for nonlinear plant The result of MPC controlling for nonlinear plant will be compared with the result of linear model MPC controlling."

"Permasalahan terbesar dalam pengendalian reaktor alir tangki berpengaduk adalah sistem yang sangat tidak linear dan multivariabel.Sistem pengendalian konvensional tidak dapat mengontrol sistem semacam ini dengan optimal, sehingga kemurnian produk yang dihasilkan rendah.Multiple Model Predictive Control (MMPC)digunakan untuk mengatasi masalah pengendalian proses yang nonlinear dan melibatkan banyak variabel. Beberapa MPC lokal digunakan pada MMPC diperoleh dengan metode yang baru dikembangkan, Representative Model Predictive Control (RMPC). Penelitian ini menggunakan model reaktor alir tangki berpengaduk yang disimulasikan dengan perangkat lunak MATLAB. Variabel yang dimanipulasi adalah suhu inlet pendingin dan konsentrasi umpan sedangkan variabel yang dikontrol adalah komposisi produk. Untuk perubahan set point konsentrasi produk dari 8,5 sampai 8,6; disarankan menggunakan MMPC 4,1,2.

---

The biggest problem in controlling Continuous Stirred Tank Reactor (CSTR) is nonlinearity in the system. Conventional control system can not optimally control this system, therefore decrease the purity of product. Multiple Model Predictive Control (MMPC), that can be used to control nonlinear and multivariable system, tried to be used on this system. Some local MPC used for MMPC based on new developed method, Representative Model Predictive Control (RMPC).

This thesis using CSTR model which is simulated by MATLAB software. The manipulated variable are cooler inlet temperature and feed concentration, and controlled variable is residual concentration. For the change of residual concentration set point from 8.5 to 8.6 change, the MMPC 4,1,2. is recommended."

"Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors"

"Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today.The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using “computationally intensive controls,” so the second part of this book addresses the solution of optimization problems in “real” time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance.The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading."

"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."