CLC (Cellular Lightweight Concrete) brick making process using neural network and extreme learning method based on microcontroller and Visual Studio.Net

In today's era the technology development is growing so fast and change the system that was originally manual into a system that all operate completely automated. By automating a production system, the number of product will increase. One of the civil materials industry that still use a lot of manual system is light brick making industry or commonly called as CLC (Cellular Lightweight Concrete) brick. A microcontroller-based light brick manufacturing system needs to be applied to optimize the production. The use of arduino mega 2560 microcontroller as the main control in the mixing automation system on this light brick making device will make it easy to do. This tool measures the weight of sand and cement using HX711 load cell sensors as a mass sensor and flow meter sensor as the required water flow sensor in the mixing process. As an actuator the Selenoid valve is useful as an automatic valve on the hole tube of water and foam, while selenoid door lock is useful as an automatic valve on sand holes, cement and stirring tube. The motor is controlled by H- Bridge Driver Motor IBT arduino. To make the system easier (Human Machine Interface) Visual Studio.Net software is applied to monitor and control the process in CLC brick making. With the prototype of automatic mixing system, the used of microcontroller in light brick making is expected to improve the industrial sectors or light brick manufacture who want to optimize the quality and quantity of its production. Automation on this system also makes all processes run fast and does not require a lot of manpower.

[1]  Adi Soeprijanto,et al.  Smart-Meter based on current transient signal signature and constructive backpropagation method , 2014, 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering.

[2]  Adi Soeprijanto.  Neural Network Optimal Power Flow(NN-OPF) based on IPSO with Developed Load Cluster Method , 2010 .

[3]  P. S. Bhandari,et al.  Cellular Lightweight Concrete Using Fly Ash , 2014 .

[4]  Kuo Lung Lian,et al.  Microgrid power flow using Homotopic and Runge-Kutta Method , 2015, 2015 IEEE 2nd International Future Energy Electronics Conference (IFEEC).

[5]  Tsai-Hsiang Chen,et al.  A distribution power flow using particle swarm optimization , 2012, PES 2012.

[6]  Adi Soeprijanto,et al.  Real-time unbalanced load flow development using direct-ZBR method and modified lambda iteration for on-line monitoring and control , 2013 .

[7]  Adi Soeprijanto,et al.  Neural network implementation for invers kinematic model of arm drawing robot , 2016, 2016 International Symposium on Electronics and Smart Devices (ISESD).

[8]  Adi Soeprijanto,et al.  Optimal Placement and Sizing of Distributed Generation for Minimize Losses in Unbalance Radial Distribution Systems Using Quantum Genetic Algorithm , 2014 .

[9]  Kuo Lung Lian,et al.  Improved Robustness of Sequential Three Phase Power Flow Using Homotopic Method , 2013 .

[10]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[11]  Adi Soeprijanto,et al.  Digital Generator Capability Curve for Improving Optimal Power Flow based on IPSO , 2013 .

[12]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[13]  Adi Soeprijanto,et al.  Rotor bars fault detection by DFT spectral analysis and Extreme Learning Machine , 2016, 2016 International Symposium on Electronics and Smart Devices (ISESD).

[14]  C. L. Liu,et al.  Robust microgrid power flow using particle swarm optimization , 2013, International School on Nonsinusoidal Currents and Compensation 2013 (ISNCC 2013).

[15]  Adi Soeprijanto,et al.  Incremental Particle Swarm Optimizer with local search for Optimal Power Flow Subjected to Digital GCC based on Neural Network , 2012 .