scholarly journals SISTEM FUZZY PADA KONTROLING AERATOR UNTUK MENINGKATKAN KUALITAS AIR KOLAM PEMBIBITAN IKAN MENGGUNAKAN SENSOR DO DAN SENSOR SUHU

2021 ◽  
Vol 8 (1) ◽  
pp. 39
Author(s):  
Andi Farmadi ◽  
Muliadi Muliadi
Keyword(s):  
Water Quality ◽  
Control System ◽  
Dissolved Oxygen ◽  
Continuous Control ◽  
Engine Control ◽  
Inference System ◽  
Analysis And Design ◽  
Oxygen Levels ◽  
Engine Control System ◽  
Smart Machine

<p><em>Dissolved oxygen levels in water will affect water quality directly and indirectly for fish life as well as conditions in the water environment, therefore, it is very important to control water quality for adequate dissolved oxygen levels, because this plays an important role in the health condition of the environmental ecosystem for fish nurseries. Researchers usually measure and monitor water quality using measuring instruments that are widely sold in the market, for conditions of decreasing dissolved oxygen levels in fish nurseries tank can usually be controlled by adding an air bubble machine to the water using an aerator machine. Giving air bubbles to water is an effort to control the conditions for the adequacy of dissolved oxygen in the water, and the best system is to carry out a continuous control system regarding water quality, sometimes the oxygen condition in the water is sufficient for the standard of dissolved oxygen in water. However, the blower blower is still running, this is less effective because it requires unnecessary electrical energy or wastes energy. Analysis of the aerator engine control system is needed to make a design as to what state the aerator engine should be turned on. Analysis of the aerator engine control system can be done by measuring the level of oxygen and water temperature in the fish nursery tank, then designing a fuzzy model with the Sugeno inference system for how long the engine must be turned on. The analysis and design of this aerator system is a proposed solution to these problems with a system of measurement and monitoring carried out intelligently by a machine, so that it is able to measure how late this aerator machine must be turned on. and the developed design is capable of being a smart machine using a fuzzy system</em></p><p><strong><em>Keywords</em></strong><em>: Fuzzy inference, aerator engine, smart system, water quality.</em></p><p><em>Kadar oksigen terlarut dalam air akan mempengaruhi kualitas air secara langsung dan tidak langsung bagi kehidupan ikan juga keadaan di lingkungan air tersebut, oleh karena itu peningkatan kualitas air untuk keadaan kecukupan kadar oksigen yang terlarut sangat penting untuk dikontrol, karena hal ini berperan penting pada kondisi kesehatan ekosistem lingkungan pembibitan ikan. </em><em>Para peneliti biasanya melakukan pengukuran dan pemantauan kualitas air dengan menggunakan alat ukur yang banyak di jual dipasaran, untuk kondisi menurunnya kadar oksigen yang terlarut pada kolam pembibitan ikan biasanya dapat di kontrol dengan menambahkan mesin gelembung udara pada air menggunakan mesin aerator. Pemberian gelembung udara pada air merupakan salah satu upaya untuk mengontrol kondisi kecukupan kadar oksigen yang terlarut di dalam air, dan sistem yang terbaik yaitu melakukan sistem kontrol secara terus menerus mengenai kualitas air, terkadang kondisi oksigen di dalam air telah mencukupi standar kecukupan oksigen terlarut pada air, namun mesin penyembur gelembung udara masih dinyalakan, hal ini menjadi kurang efektif sebab akan membutuhkan energi listrik yang tidak semestinya atau terjadinya pemborosan energi. Analisis sistem pengontrolan mesin aerator dibutuhkan untuk melakukan desain seperti apa sebaiknya keadaan mesin aerator dihidupkan. Analisis sistem pengontrolan mesin aerator ini dapat dilakan dengan mengukur tingkat kadar oksigen dan suhu air pada kolam pembibitan ikan, kemudian melakukan perancangan model fuzzy dengan sistem inferensi sugeno seberapa lama mesin harus dihidupkan. Analisis dan desain sistem aerator ini merupakan usulan solusi permasalahan tersebut dengan sistem pengukuran dan pemantauan dilakukan secara cerdas oleh mesin, sehingga mampu mengukur seberapa lalma mesin aerator ini harus dihidupkan desain alat ini juga diharapkan mampu memberikan solusi peningkatan kualitas air pada pembibitan ikan dan diharapan pula analisis dan desain yang dikembangkan ini mampu menjadi mesin cerdas dengan menggukan sistem fuzzy</em></p><p><strong><em>Kata kunci</em></strong><em> : Fuzzy inferensi, mesin aerator, Sistem cerdas, kualitas air.</em></p>

scholarly journals Co-Simulation of Distributed Engine Control System and Network Model using FMI & SCNSL

2015 ◽  
Vol 48 (16) ◽  
pp. 261-266 ◽  
Author(s):  
Nicolai Pedersen ◽  
Jan Madsen ◽  
Morten Vejlgaard-Laursen
Keyword(s):  
Control System ◽  
Network Model ◽  
Engine Control ◽  
Engine Control System

A study of water quality in an urban river and potential improvement using a prototype instream aerator

1989 ◽  
Vol 16 (3) ◽  
pp. 308-316 ◽  
Author(s):  
C. A. Town ◽  
D. S. Mavinic ◽  
B. Moore
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Data Base ◽  
Chlorophyll A ◽  
Late Summer ◽  
Urban River ◽  
Agricultural Activity ◽  
Fish Kills ◽  
Low Dissolved Oxygen ◽  
Oxygen Levels

Urban encroachment and intensive agricultural activity within the Serpentine–Nicomekl watershed (near Vancouver, B.C.) have caused a series of fish (salmon) kills on the Serpentine River since 1980. Low dissolved oxygen was responsible for these kills. This field project investigated some of the dynamic chemical and biological relationships within the river, as well as the use of an instream aerator as a temporary, in situ, water quality improvement measure. Weekly sampling for a 6-month period during the latter half of 1985 established a solid data base for deriving and interpreting meaningful interrelationships. A strong correlation between chlorophyll a and dissolved oxygen levels before the algae die-off supported the hypothesis that algae blooms dying in the fall could create a serious oxygen demand. Because of these environmental conditions, the river is unable to sustain healthy dissolved oxygen levels during this period. As such, a prototype, 460 m artificial aeration line was designed, installed, and monitored to evaluate its potential for alleviating low dissolved oxygen conditions and improving overall water quality during the critical fall period.The instream aerator ran continuously for over 2 months, starting in September 1985. Despite better-than-expected weather conditions (i.e., cool, wet weather) and relatively high dissolved oxygen levels during the fall of 1985, the data base appeared to support the use of this prototype aeration unit as a means of "upgrading" a stretch of an urban river subject to periodic, low dissolved oxygen levels. As a result, a 2-year follow-up study and river monitoring was initiated. In both 1986 and 1987, late summer and early fall river conditions resulted in the potential for serious salmon kills, due to higher-than-normal river temperatures and very low dissolved oxygen. In both instances, the instream aerator prevented such fish kills in a key stretch of the river. Expansion of the system to include other critical stretches of the Serpentine and other urban river systems, near Vancouver, is being considered. Key words: algae, aerator, chlorophyll a, eutrophic, fish kills, instream aeration, river improvement, urban river.

Advanced Control System Considerations for Small Shaft-Type Aircraft Gas Turbines

1970 ◽  
Author(s):  
D. A. Prue ◽  
T. L. Soule
Keyword(s):  
Control System ◽  
Gas Turbines ◽  
Evaluation Criteria ◽  
Open Loop ◽  
Engine Control ◽  
Gas Generator ◽  
Engine Control System ◽  
Power Turbine ◽  
Advanced Control ◽  
Temperature Load

The next generation of free-turbine engines in the 2 to 5-lb/sec airflow class will undergo vast improvements in performance and efficiency. The improvements will be achieved concurrent with overall reductions in size and weight. Effort is required at optimization and miniaturization of the engine control system to keep pace with these improvements. This paper describes a conceptual design of an advanced engine control system for this class of engine. It provides gas generator and power turbine control with torque, temperature, load sharing and overspeed limiting functions. The control system was concepted to accommodate, with minimum hardware changes, such variants as regenerative cycle and/or variable power turbine geometry. In addition, considerations for closed and open loop modes of control and fluidic, electronic and hydromechanical technologies were studied to best meet a defined specification and a weighted set of evaluation criteria.

Toyota's New Microprocessor-Based Diesel Engine Control System for Passenger Cars

1985 ◽  
Vol IE-32 (4) ◽  
pp. 289-293 ◽  
Author(s):  
Mitsuo Kawai ◽  
Hideo Miyagi ◽  
Jiro Nakano ◽  
Yoshihiko Kondo
Keyword(s):  
Control System ◽  
Diesel Engine ◽  
Engine Control ◽  
Passenger Cars ◽  
Engine Control System

scholarly journals Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

2011 ◽  
Vol 14 (1) ◽  
pp. 167-179 ◽  
Author(s):  
Vesna Ranković ◽  
Jasna Radulović ◽  
Ivana Radojević ◽  
Aleksandar Ostojić ◽  
Ljiljana Čomić
Keyword(s):  
Experimental Data ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Network ◽  
Inference System ◽  
Fuzzy Models ◽  
Highly Nonlinear ◽  
Input Variables

Predicting water quality is the key factor in the water quality management of reservoirs. Since a large number of factors affect the water quality, traditional data processing methods are no longer good enough for solving the problem. The dissolved oxygen (DO) level is a measure of the health of the aquatic system and its prediction is very important. DO dynamics are highly nonlinear and artificial intelligence techniques are capable of modelling this complex system. The objective of this study was to develop an adaptive network-based fuzzy inference system (ANFIS) to predict the DO in the Gruža Reservoir, Serbia. The fuzzy model was developed using experimental data which were collected during a 3-year period. The input variables analysed in this paper are: water pH, water temperature, total phosphate, nitrites, ammonia, iron, manganese and electrical conductivity. The selection of an appropriate set of input variables is based on the building of ANFIS models for each possible combination of input variables. Results of fuzzy models are compared with measured data on the basis of correlation coefficient, mean absolute error and mean square error. Comparing the predicted values by ANFIS with the experimental data indicates that fuzzy models provide accurate results.

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