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All Journal Jurnal Teknik Industri Journal of Mechanical Engineering Science and Technology Journal of Energy, Mechanical, Material and Manufacturing Engineering
Achmad Fauzan Hery
University Of Muhammadiyah Malang
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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The Role of Flue Gas Inhibitor on Stabilizing Heptane Flame in Meso Scale Combustor Achmad Fauzan Hery Soegiharto; Ali Mokhtar; Sudarman; Satworo Adiwidodo
JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 6 No. 2 (2021)
Publisher : University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jemmme.v6i2.19458

Abstract

Mesoscale combustor is one of the components that serves to generate heat on a micro power generator. As one of the components of a micro power generator, meso scale combustor serves to supply heat through the combustion process. The stability of the flame in the combustion chamber meso scale combustor is influenced by the temperature of the combustion chamber. One way to maintain a high temperature in the combustion chamber is to insert a flue gas stainless steel mesh resistor.. This research aims to prove the role of flue gas mesh resistors in stabilizing the flame on the meso scale combustor... The heptane liquid fuel flame was successfully stabilized at an equivalence ratio of ɸ 0.81 – 1.29 and a reactant flow velocity of 26.12 – 36.83 cm/s. The higher the rate of reactant flow, the higher the flame temperature until it reaches 502ºC. Combustor with flue gas mesh resistor is 10 mm away has a flammability limit that is not wider than a combustor without flue gas mesh resistor.
Pemanfaatan Biogas/Landfillgas Sebagai Bahan Bakar Mesin Bensin 1 Silinder 4 Langkah Achmad Fauzan Hery; Zamzami Septiropa; Selly Riansyah; Faizal Romadhi
Jurnal Teknik Industri Vol. 12 No. 2 (2011): Agustus
Publisher : Department Industrial Engineering, University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1616.022 KB) | DOI: 10.22219/JTIUMM.Vol12.No2.162-168

Abstract

Motor gasoline also proven to be turned on by using biogas. This is done through the addition of a simple regulator to biogas, and air mixer - biogas. In practice, though not obtain maximum performance, modifiers remain to be done without changing the ignition time. Previously, the experiment was done first using LPG, LPG-and then use a mixture of biogas and biogas pure. Machine can be turned on using biogas with methane content of 56-60%. Engine fuel or motor fuel of biogas that is used in the experiment can generate electricity to turn the lights up to 300 watts. A quarter of its normal capacity when using premium fuel / gasoline. Optimal load occurs at 150 watts means the power load 150 watts, the conversion of biogas energy to be the highest power of 230 watt/m3 biogas. At 150 watts of loading the fuel consumption is 0.000097333 liters / watt.
Study on Predictive Maintenance of V-Belt in Milling Machines Using Machine Learning Reza Aulia Rahman; Mohammad Faishol Erikyatna; Achmad Fauzan Hery Soegiharto
Journal of Mechanical Engineering Science and Technology (JMEST) Vol 6, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um016v6i22022p085

Abstract

Towards industry 4.0, monitoring the degradation of machine tools’ components becomes a key feature so that smooth productivity is achieved. To preserve the functionality and performance of the machine tools, proper maintenance activities must be planned and carried out. V-belt is important component in machine tools that transmits power from the electric motor spindle in order to machine to work and cut desired material properly. The purpose of this research is to develop a predictive maintenance system for v-belt milling machine Krisbow 31N2F using machine learning. The machine learning algorithm models using multiple and simple linear regression algorithm was developed in an open-source program. The test results show that the machine learning model has a high accuracy value in both the training data and the testing data. The multiple linear regression model has MSE value of 5.8830x10-6 and MAE value of 0.002. The Simple linear regression model has an MSE value of 0.0004x10-6 and MAE value of 0.162. The results shows that the use of the linear regression algorithm as a support for determining the prediction of RUL v-belt milling machine model 31N2F (BS) is successfully carried out.
Co-Authors Ali Mokhtar Faizal Romadhi Mohammad Faishol Erikyatna Reza Aulia Rahman Satworo Adiwidodo Selly Riansyah Sudarman Sudarman Zamzami Septiropa