scholarly journals PROTOTYPE SISTEM KENDALI KADAR KEPEKATAN ASAP PADA SMOKING ROOM DENGAN METODE FUZZY LOGIC BERBASIS ARDUINO

2020 ◽  
Vol 3 (2) ◽  
pp. 32-37
Author(s):  
M. Sunan Ishfahani ◽  
Nur Hanifah Yuninda ◽  
Purwanto Gendroyono
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Research And Development ◽  
Fuzzy Logic Controller ◽  
Concentration Level ◽  
Requirements Analysis ◽  
Smoke Detection ◽  
Fuzzy Logic Method ◽  
Analysis Design ◽  
And Control

The purpose of this research is to make prototype of smoke concentration levels control system in smoking room by fuzzy logic method based on arduino. The prototype is able to detect and control the smoke to stay constantly in allowed threshold to the smoking room and provide periodic information about the concentration level in that smoking room automatically. This research uses Research and Development Method, which includes requirements analysis, design, basic implementation into prototype form. Requirements analysis is based on the lack of smoke detection and control systems in smoking rooms. The design of the control system in this study is applied to fuzzy logic controller. This research emphasizes more on the basic implementation of the desired prototype by standards testing such as output voltage testing and fuzzyfication testing that have not arrived at the stage of efficiency testing. The results show that the system will be put into danger condition if the concentration of smoke reaches 80 PPM. At that level, the voltage measured by the sensor is in the range of 3.19 to 3.41 VDC with an average voltage increase of 0.035 to 0.04 VDC per PPM. Error in fuzzyfication testing is 0.04% and 0.08% based on calculation. Abstrak Tujuan penelitian ini adalah membuat prototype sistem kendali kadar kepekatan asap pada smoking room dengan metode fuzzy logic berbasis arduino. Alat ini dapat mendeteksi dan mengendalikan asap agar tetap pada ambang batas yang diperbolehkan pada smoking room serta memberikan informasi secara periodik tentang kadar kepekatan asap yang terdapat pada smoking room secara otomatis. Penelitian ini menggunakan Metode Penelitian dan Pengembangan (Research and Development) yang meliputi analisis kebutuhan, perancangan, implementasi dasar dalam bentuk prototype dan pengujian. Analisis kebutuhan didasari karena jarang terdapatnya sistem deteksi dan kendali asap pada smoking room. Perancangan sistem kendali pada penelitian ini diterapkan fuzzy logic controller. Penelitian ini lebih menekankan pada implementasi dasar dari prototype yang diinginkan dengan pengujian standar seperti pengujian tegangan keluaran dan pengujian fuzzyfikasi yang belum sampai pada tahap pengujian efisiensi. Hasil penelitian menunjukkan bahwa sistem akan mengindikasikan sebuah bahaya jika kadar kepekatan asap mencapai 80 PPM. Pada kadar tersebut, tegangan yang terukur oleh sensor berada pada kisaran 3,19 sampai 3,41 VDC dengan rata-rata kenaikan tegangan sebesar 0,035 sampai 0,04 VDC per PPM. Error pada pengujian fuzzyfikasi adalah sebesar 0,04 % dan 0,08 % yang didasarkan pada perhitungan.

Development and evaluation of a brine mining equipment monitoring and control system using Wireless Sensor Network and fuzzy logic

2017 ◽  
Vol 40 (6) ◽  
pp. 2062-2081 ◽  
Author(s):  
Liu He ◽  
Cui Yan ◽  
Yanqing Duan ◽  
Stankovski Stevan ◽  
Zhang Xiaoshuan ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Information And Communication Technologies ◽  
Fuzzy Logic Controller ◽  
Salt Lake ◽  
Mining Equipment ◽  
Time Data ◽  
Remote Communication ◽  
Underground Brine ◽  
And Control

The brine mining equipment failure can seriously affect the productivity of the salt lake chemical industry. Traditional monitoring and controlling method mainly depends on manned patrol that is offline and ineffective. With the rapid advancement of information and communication technologies, it is possible to develop more efficient online systems that can automatically monitor and control the mining equipment and to prevent equipment damage from mechanical failure and unexpected interruptions with severe consequences. This paper describes a Wireless Monitoring and feedback fuzzy logic-based Control System (WMCS) for monitoring and controlling the brine well mining equipment. Based on the field investigations and requirement analysis, the WMCS is designed as a Wireless Sensors Network module, a feedback fuzzy logic controller, and a remote communication module together with database platform. The system was deployed in existing brine wells at demonstration area without any physical modification. The system test and evaluation results show that WMCS enables to track equipment performance and collect real-time data from the spot, provides decision support to help workers overhaul the equipment and follows the deployment of fuzzy control in conjunction with remote data logging. It proved that WMCS acts as a tool to improve management efficiency for mining equipment and underground brine resources.

Fuzzy Logic in Hole Drilling of Composites

1994 ◽  
Author(s):  
Aditya Thadani ◽  
Athamaram H. Soni
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Thrust Force ◽  
Cutting Speed ◽  
Research Data ◽  
Theoretical Research ◽  
Hole Drilling ◽  
Tool Geometry ◽  
Drilling Process ◽  
And Control

Abstract Experimental and theoretical research data was utilized in building a Fuzzy Logic Controller model applied to simulate the drilling process of composite materials. The objective is to have a better understanding and control of delamination of composites during the drilling process and at the same time to improve the hole finish by controlling fraying and splintering. By controlling the main issues in the drilling process such as feed rate, cutting speed, thrust force, and torque generated in addition to the tool geometry, it is possible to optimize the drilling process avoiding the conventionally encountered problems.

Artificial Neural Networks Used As a Rule Selector for Fuzzy Logic Controllers

1993 ◽  
Author(s):  
Shou-Heng Huang ◽  
Ron M. Nelson
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Absolute Error ◽  
Operating Conditions ◽  
Delta Rule ◽  
Rule Sets ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Set Up ◽  
And Control

Abstract A feedforward, three-layer, partially-connected artificial neural network (ANN) is proposed to be used as a rule selector for a rule-based fuzzy logic controller. This will allow the controller to adapt to various control modes and operating conditions for different plants. A principal advantage of an ANN over a look up table is that the ANN can make good estimates to fill in for missing data. The control modes, operating conditions, and control rule sets are encoded into binary numbers as the inputs and outputs for the ANN. The General Delta Rule is used in the backpropagation learning process to update the ANN weights. The proposed ANN has a simple topological structure and results in a simple analysis and relatively easy implementation. The average square error and the maximal absolute error are used to judge if the correct connections between neurons are set up. Computer simulations are used to demonstrate the effectiveness of this ANN as a rule selector.

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