Hasil Pencarian  ::  Simpan CSV :: Kembali

"Methods of controlling mass concrete temperatures range from relatively simple to complex and from inexpensive too costly. Depending on a particular situation, it may be advantageous to use one or more methods over others. based on the author?s 50 years of personal experience in designing mass concrete structures, thermal stresses and temperature control of mass concrete provides a clear and rigorous guide to selecting the right techniques to meet project-specific and financial needs. new techniques such as long time superficial thermal insulation, comprehensive temperature control, and MgO self-expansive concrete are introduced."

"This volume covers diverse areas of applied mathematics, continuum mechanics, stress analysis, and mechanical design. This work treats a number of topics not presented in other books on thermal stresses, for example: theory of coupled and generalized thermoelasticity, finite and boundary element method in generalized thermoelasticity, thermal stresses in functionally graded structures, and thermal expansions of piping systems.The book starts from basic concepts and principles, and these are developed to more advanced levels as the text progresses. Nevertheless, some basic knowledge on the part of the reader is expected in classical mechanics, stress analysis, and mathematics, including vector and cartesian tensor analysis.This 2nd enhanced edition includes a new chapter on Thermally Induced Vibrations. The method of stiffness is added to Chapter 7. The variational principle for the Green-Lindsay and Green-Naghdi models have been added to Chapter 2 and equations of motion and compatibility equations in spherical coordinates to Chapter 3. Additional problems at the end of chapters were added. "

"In this paper, the behavior of concrete subjected to biaxial compression and uniaxial fatigue is studied. The tests are performed on 100 x 100 x 100 mm cube specimens. Three Lateral stress levels ( a is defined as the ratio of stress in the horizontal direction that keeps constant during the testing procedure to the uniaxial compressive strength) 0, 0.25 and 0.5 are performed in the tests. The experimental stress-strain curves have been achieved. Empirical Relationship are proposed for predicting the maximum stress level as a function of lateral stress and fatigue life. The experimental results show that an increase of the horizontal stress leads to a change of failure mode and an increase of the maximum vertical load-carrying capacity . Also, the observation of the failure modes indicates that concrete possesses similar failure patterns under monotonic and fatigue compression is presented. Comparisons of the theoretical stress-strai curves with the experimental data indicate a good agreement."

"Skripsi ini bertujuan untuk mengetahui persebaran temperatur pada beton massa yang ditinjau secara horizontal pada raft foundation Rasuna Tower. Parameter yang ditinjau dalam makalah ini adalah initial temperatur, temperatur maksimum, perbedaan temperatur dan tegangan yang terjadi pada beton massa secara horizontal. Batasan temperatur maksimum dan perbedaan temperatur yang diizinkan pada umumnya adalah 70°C dan perbedaan temperatur ≥ 20 °C. perbedaan temperatur yang terjadi tergantung dari pengendalian temperatur yang dilakukan. Temperatur puncak yang terjadi ± 80 °C. Temperatur puncak yang terjadi tergantung dari initial temperature dan mix design. Tegangan yang terjadi dipengaruhi oleh perubahan temperatur pada nodal. Semakin jauh jaraknya dari tepi maka semakin tinggi temperatur yang terjadi karena pelepasan panas yang semakin lama. Namun pada penyebaran tegangan, nilainya tidak dipengaruhi oleh jarak ke tepi beton. Tapi sangat dipengaruhi oleh lapisan insulasi dan terdekat dengan titik yang ditinjau.

---

This final report aims to determine the temperature distribution in mass concrete terms horizontally on a raft foundation Rasuna Tower. The parameters of interest in this paper is the initial temperature, maximum temperature, temperature difference and stress happened on mass concrete horizontally. The maximum temperature and the temperature difference that allowed in general is 70°C and not exceed 20°C. Temperature difference that occur depends on the temperature control is performed. Peak temperature occurred ± 80°C. Peak temperature is dependent on the initial temperature and mix design. Stress that occurs is influenced by changes in the nodal temperature. Increasing distance from the edge makes higher temperatures due to the longer heat release. The deployment stress is not influenced by the distance to the edge of the concrete but is strongly influenced by the insulation layer and the closest to the point of being reviewed."

"Di dalam dunia industri, operasi pencampuran banyak digunakan untuk mengolah bahan mentah menjadi suatu produk seperti minyak bumi, bahan kimia, dan lainnya. Biasanya, pada plant skala industri digunakan PID kontroler sebagai sistem pengendaliannya, akan tetapi PID kontroler ini akan menjadi kurang baik ketika menghadapi sistem non-linear, sehingga pada penilitian ini dirancang suatu sistem kendali berbasis neural network yang diharapkan dapat memberikan performa yang lebih baik dan efisien dibandingkan PID konvensional. Model plant yang digunakan untuk simulasi di dalam penelitian ini adalah proses pencampuran air, dimana temperatur dan level air akan dikendalikan. Dibuat dua jenis sistem pengendali neural network (NN) dengan perbedaan pada input-nya, yaitu NN dengan input SP, PV(n), PV(n-1) dan NN dengan input SP, error, dan perubahan error. Kedua sistem pengendali neural network ini dibuat dengan menggunakan metode feed-forward neural network dan simulasinya dibuat dengan menggunakan Simulink. Berdasarkan hasil pengujian, dapat disimpulkan bahwa sistem pengendalian dengan menggunakan neural network memberikan performa yang lebih baik jika dibandingkan dengan sistem pengendalian PID konvensional, yaitu dengan settling time dan rise time yang lebih cepat, serta menghasilkan respon sistem yang tidak memiliki overshoot sama sekali.

---

In the industrial world, blending operations are widely used to process raw materials into products such as petroleum, chemicals, and others. Usually, in industrial-scale plants, the PID controller is used as a control system, but this controller will be less good when dealing with non-linear systems. In this study, a neural network-based control system is expected to provide better and more efficient performance compared to conventional PID control. The plant model used for simulation in this study is the process of mixing water, where the temperature and water level will be controlled. Created two types of neural network (NN) control systems with differences in the input, the first is a NN with SP, PV(n), PV(n-1) for the input, and the second is a NN with SP, error, and change of error for the input. Both of these neural network control systems are made using a feed-forward neural network method, and the simulation was created by using Simulink. Based on the test results, it can be concluded that the control system using a neural network provides better performance when compared to conventional PID control systems with a faster settling time and rise time, and produces a system response that has no overshoot at all."

"Significant changes over the past decade in computing technology, along with widespread deregulation of electricity industries, have impacted on power plant operations while affording engineers the opportunity to introduce monitoring and plant-wide control schemes which were previously unfeasible. Contributors of world-class excellence are brought together in Thermal Power Plant Simulation and Control to illustrate how current areas of research can be applied to power plant operation, leading to enhanced unit performance, asset management and plant competitiveness through intelligent monitoring and control strategies."

"Proses Thermal Mixing adalah jenis dari proses pencampuran yang penting di berbagai industri, seperti industri pangan, pupuk, farmasi, material sampai petrochemical. Proses Thermal Mixing merupakan proses Multi input multi ouput (MIMO), karena bekerja dengan mengendalikan dua flow air panas dan air dingin untuk mengendalikan temperatur dan level campuran. Meskipun memiliki respon yang kurang baik untuk mengendalikan MIMO, namun PID masih banyak digunakan karena kesederhanaannya. Algoritma non konvensional yang lebih baik seperti fuzzy control memiliki kerumitan yang tinggi dibanding PID. Algoritma Adaptive Fuzzy PID Controller (AFPIDC) merupakan gabungan dari keduanya, memiliki basis PID yang cukup sederhana namun ditambahkan aspek Fuzzy untuk mempercepat pengendalian dengan cara mengubah konstanta PID secara real-time (on the fly). Algoritma AFPIDC ini diterapkan pada simulasi sistem pengendalian temperatur dan level air pada proses water Thermal Mixing dan dilakukan pada program MATLAB/SIMULINK di PC. Fuzzy yang digunakan memiliki dua input berupa error dan perubahan error, dan memiliki tiga output berupa perubahan nilai konstanta PID. Pengujian sistem dilakukan dengan simulasi perubahan setpoint dan gangguan berupa kebocoran flow. Dari hasil pengujian sistem, pengendali AFPIDC memiliki performa yang lebih baik dari PID dalam mengendalikan temperatur dan level pada sistem. Dalam pengendalian temperatur, didapatkan nilai settling time PID sebesar 830 detik, AFPIDC sebesar 328 detik dan untuk nilai overshoot PID 6,3% dan AFPIDC 0%. Untuk pengendalian level didapatkan settling time PID 3221 detik dan AFPIDC 235 detik dengan nilai overshoot PID 10,5% dan AFPIDC 0%. Dari pengujian sistem terhadap gangguan kebocoran, pengendali temperatur membutuhkan waktu untuk kembali stabil pada PID 780 detik, AFPIDC 250 detik. Sedangkan untuk pengendalian level untuk kembali stabil membutuhkan waktu PID 4510 detik, AFPIDC 225 detik.

---

The Thermal Mixing Process is a type of mixing process that is important in various industries, such as the food, fertilizer, pharmaceutical, material to petrochemical industries. The Thermal Mixing Process is a multiple-input multiple-output process (MIMO), because it works by controlling hot water and cold-water flows to control the temperature and level of the mixture. Although it has a poor response to control MIMO system, PID is still widely used because of its simplicity. There are some better control algorithm, such as fuzzy control, but have higher complexity than PID. The Adaptive Fuzzy PID Control (AFPIDC) algorithm is a combination of the two, has a simple PID basis with added Fuzzy aspects to speed up control by changing the PID constant in realtime. The AFPIDC algorithm is applied to the simulation of temperature and water level control systems in the process of water Thermal Mixing and is done on the MATLAB/SIMULINK program on a PC. The fuzzy algorithm uses two inputs in the form of errors and changes in errors and has three outputs in the form of changes in the value of the PID constant. System testing is done by simulating setpoint changes and disruption in the form of leakage flow. From the results of system testing, AFPIDC controllers have better performance than PID in controlling temperature and level in the system. In temperature control, the PID settling time is 830 seconds, AFPIDC is 328 seconds and the PID overshoot is 6,3% and AFPIDC is 0%. In level control, the settling time of PID is 3221 seconds while AFPIDC is 235 seconds with PID overshoot is 10,5% while AFPIDC 0%. From testing the system with leakage disturbance, the temperature controller needs time to regain stability at PID 780 seconds, AFPIDC 250 seconds. Meanwhile the level controlling stabilizes at PID 4510 seconds, and AFPIDC at 225 seconds."