Perancangan Sistem Pengontrolan Level Pada Steam Drum Waste Heat Boiler Berbasis Adaptive Network Fuzzy Inference System (ANFIS)

Unit boiler sangatlah penting pada industri pupuk seperti di PT. Petrokimia Gresik, khususnya dalam produksi steam. Kualitas steam yang dihasilkan steam sangat dipengaruhi oleh aliran gas buang panas yang dihasilkan dari pembakaran di dalam turbin gas dan juga dipengaruhi oleh level dari drum itu sendiri. Untuk bisa menghasilkan kualitas steam yang baik, maka berbagai parameter di dalamnya harus dapat dikontrol dengan baik, mengingat karakteristik dinamiknya yang sering berubah menyesuaikan beban proses yang ada. Salah satu komponen kontrol yang penting adalah High Pressure Steam Drum (HP drum). Parameter kontrol yang harus dijaga dengan baik adalah level air pada steam drum. Oleh karena itu dalam penelitian ini, dengan memperhatikan struktur agar tidak terlalu mengganggu sistem yang telah ada, maka ditawarkan solusinya menggunakan kontrol adaptif yaitu dengan merancang suatu sistem pengontrolan level pada steam drum waste heat boiler berbasis neuro fuzzy jenis Adaptive Neuro Fuzzy Inference System (ANFIS). Berdasarkan hasil simulasi dan pengujian, maka dapat diambil kesimpulan bahwa pemodelan sistem berbasis pada neuro fuzzy dengan 3 masukan, 1 keluaran serta model dinamika invers plant pada HP steam drum telah menunjukkan hasil yang cukup baik. Selain itu performansi pengontrol neuro fuzzy dalam kecepatan beradaptasi dan kemampuan belajar cukup baik dalam mengejar set point

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

A Fuzzy Logic-based Tuning Approach of PID Control for Steam Turbines for Solar Applications

Abstract This work aims at improving the control concept based on PID controller by jointly exploiting experience and knowledge on the system behaviour and artificial intelligence. A Concentrated Solar Power Plant (CSPP) system has been modelled and a stability and performance analysis has been carried out, focusing on power control loop, which is normally based on standard PID. A hybrid fuzzy PID approach is proposed to improve the steam turbine governor action and its performance are compared to the classical PID tuned according to three different approaches. Compared to the classic PID, the PID fuzzy logic controller extends the simplicity of PID and adapts the control action at actual operating condition by providing the system with a sort of "decision-making skill". The possibility to design implementable algorithms on PLC, which have stringent computational speed and memory requirements, has been explicitly taken into account in the developed work

Application of Intelligent Computational Techniques in Power Plants:A Review

Growing worldwide demand for energy leads to increasing the levels of challenge in power plants management. These challenges include but are not limited to complex equipment maintenance, power estimation under uncertainty, and energy optimisation. Therefore, efficient power plant management is required to increase the power plant’s operational efficiency. Conventional optimisation tools in power plants are not reliable as it is challenging to monitor, model and analyse individual and combined components within power systems in a plant. However, intelligent computational tools such as artificial neural networks (ANN), nature-inspired computations and meta-heuristics are becoming more reliable, offering a better understanding of the behaviour of the power systems, which eventually leads to better energy efficiency. This paper aims to provide an overview of the development and application of intelligent computational tools such as ANN in managing power plants. Also, to present several applications of intelligent computational tools in power plants operations management. The literature review technique is used to demonstrate intelligent computational tools in various power plants applications. The reviewed literature shows that ANN has the greatest potential to be the most reliable power plant management tool

Temperature Control System in Closed House for Broilers Based on ANFIS

 Indonesia is a tropical country with high ambient temperatures for broilers since daily temperature reaches an average daily temperature of 360C (maximum) and 320 C (minimum); whereas the optiml temperature for broilers is in the range of 28-300C. Thefefore, midle or large scale broiler industries have been using a control system to maintain the optimal temperature within a broiler house. Therefore, the role of a control system for regulating environmental parameters, not only temperature but also humidity, light intensity, and amonia content level, is very critical and relevant for better broiler production. This study aims to design an ANFIS control system for controlling the temperature inside a broiler house (closed house) for broiler. Data is collected at three different periods of the starter period (5 days): 29.50C-30.900C, a period of 25 days is a grower-29.0C 34.20C, and the finisher of 30 days is obtained 33.20C. Set point control simulation using the same temperature 290C for starter, grower and finisher period. The simulation results show the output in a closed house temperature fluctuates around set point the 290C-340C

Perancangan Sistem Pengontrolan Level Pada Steam Drum Waste Heat Boiler Berbasis Adaptive Network Fuzzy Inference System (ANFIS)

Unit boiler sangatlah penting pada industri pupuk seperti di PT. Petrokimia Gresik, khususnya dalam produksi steam. Kualitas steam yang dihasilkan steam sangat dipengaruhi oleh aliran gas buang panas yang dihasilkan dari pembakaran di dalam turbin gas dan juga dipengaruhi oleh level dari drum itu sendiri. Untuk bisa menghasilkan kualitas steam yang baik, maka berbagai parameter di dalamnya harus dapat dikontrol dengan baik, mengingat karakteristik dinamiknya yang sering berubah menyesuaikan beban proses yang ada. Salah satu komponen kontrol yang penting adalah High Pressure Steam Drum (HP drum). Parameter kontrol yang harus dijaga dengan baik adalah level air pada steam drum. Oleh karena itu dalam penelitian ini, dengan memperhatikan struktur agar tidak terlalu mengganggu sistem yang telah ada, maka ditawarkan solusinya menggunakan kontrol adaptif yaitu dengan merancang suatu sistem pengontrolan level pada steam drum waste heat boiler berbasis neuro fuzzy jenis Adaptive Neuro Fuzzy Inference System (ANFIS). Berdasarkan hasil simulasi dan pengujian, maka dapat diambil kesimpulan bahwa pemodelan sistem berbasis pada neuro fuzzy dengan 3 masukan, 1 keluaran serta model dinamika invers plant pada HP steam drum telah menunjukkan hasil yang cukup baik. Selain itu performansi pengontrol neuro fuzzy dalam kecepatan beradaptasi dan kemampuan belajar cukup baik dalam mengejar set point

Improvement of existing coal fired thermal power plants performance by control systems modifications

This paper presents possibilities of implementation of advanced combustion control concepts in selected Western Balkan thermal power plant, and particularly those based on artificial intelligence as part of primary measures for nitrogen oxide reduction in order to optimise combustion and to increase plant efficiency. Both considered goals comply with environmental quality standards prescribed in large combustion plant directive. Due to specific characterisation of Western Balkan power sector these goals should be reached by low cost and easily implementable solution. Advanced self-learning controller has been developed and the effects of advanced control concept on combustion process have been analysed using artificial neural-network based parameter prediction model. (c) 2013 Elsevier Ltd. All rights reserved

Doubly-fed induction generator used in wind energy

Wound-rotor induction generator has numerous advantages in wind power generation over other generators. One scheme for wound-rotor induction generator is realized when a converter cascade is used between the slip-ring terminals and the utility grid to control the rotor power. This configuration is called the doubly-fed induction generator (DFIG). In this work, a novel induction machine model is developed. This model includes the saturation in the main and leakage flux paths. It shows that the model which considers the saturation effects gives more realistic results. A new technique, which was developed for synchronous machines, was applied to experimentally measure the stator and rotor leakage inductance saturation characteristics on the induction machine. A vector control scheme is developed to control the rotor side voltage-source converter. Vector control allows decoupled or independent control of both active and reactive power of DFIG. These techniques are based on the theory of controlling the B- and q- axes components of voltage or current in different reference frames. In this work, the stator flux oriented rotor current control, with decoupled control of active and reactive power, is adopted. This scheme allows the independent control of the generated active and reactive power as well as the rotor speed to track the maximum wind power point. Conventionally, the controller type used in vector controllers is of the PI type with a fixed proportional and integral gain. In this work, different intelligent schemes by which the controller can change its behavior are proposed. The first scheme is an adaptive gain scheduler which utilizes different characteristics to generate the variation in the proportional and the integral gains. The second scheme is a fuzzy logic gain scheduler and the third is a neuro-fuzzy controller. The transient responses using the above mentioned schemes are compared analytically and experimentally. It has been found that although the fuzzy logic and neuro-fuzzy schemes are more complicated and have many parameters; this complication provides a higher degree of freedom in tuning the controller which is evident in giving much better system performance. Finally, the simulation results were experimentally verified by building the experimental setup and implementing the developed control schemes

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

Alternative Water Level Controller using Artificial Intelligence for Industrial Drum Boiler.

This work is based onthe paper entitled 'Overcoming the shrinkandswell effect in water level controlstrategy on industrial drum boiler' [1], The basic function of this boiler is to produce steam. The steam will allow the turbine to function and therefore, will generate electricity to users. This paper illustrates the optimisation of loadfollowing scheme by applying 3-element controller scheme to regulate the drumboiler's water level. The PID controller is, by far the most commonly used for controlling and monitoring purposes in process plants. Nevertheless, the performance specifications ofa PID controllercould be further improved. This FinalYear Project entitled 'Alternative WaterLevel Controller usingArtificial Intelligence (AI)forIndustrialDrum Boiler'. It applies neural network, neuro-fuzzy and pre-processing to comparethe control performance ofthe steam pressureand water level ofthe boiler model. The objective of the project is to design and construct software by using Matlab, which can simulate and enhance thecontroller ofwater level andsteam pressure indrum boiler. The Artificial Intelligence approach makes it easier to conceptualize and implement control systems. Theprocess is reduced to a setofvisualizable steps. Thisis a very important point. Actually implementing a control system, even a simple control system, is more difficult than it appears. Unexpected aberrations and physical anomalies inevitably occur. In reading about AI control applications in industry, one of the significant points that stand out is: AIis usedbecause it shortens the time for engineering development. AI enables engineers to configure systems quickly without extensive experimentation andto make use of information from expert human operators who have beenperforming the task manually