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All Journal CHEESA: Chemical Engineering Research Articles Jurnal Indonesia Sosial Teknologi
Pandjaitan, M.M. Lanny W.
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Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Effect of EDTA Addition on Acidizing Treatment Process Fitriyanti, Reno; Pandjaitan, M.M. Lanny W.; Lukas, Lukas; Harlis, Gilang Bagaskara; Wahyudi, Agus; Fatimura, Muhrinsyah
CHEESA: Chemical Engineering Research Articles Vol. 6 No. 2 (2023)
Publisher : Universitas PGRI Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25273/cheesa.v6i2.15647.95-104

Abstract

Acidizing treatment is commonly used to solve scale problem on production equipment. In this process, Hydrochloric acid (HCl) is often used to treat CaCO3 scale, posing the risk of pipe corrosion due to its high corrosive characteristics. Thus, the purpose of this study was to determine the impact of adding EDTA additive into HCl solution during the acidifying treatment procedure. The methodology included various stages such as scale identification, chemical scale removal test using 7.5% and 15% HCL solution, 15% HCl solution test with EDTA as an additive, and corrosion determination using corrosion coupon. The results showed that 15% HCl solution was very effective in removing CaCO3 scale but had a high corrosion rate of 186.255 mpy. Furthermore, the addition of 10 mL EDTA solution as an additive removed scale and reduced corrosion rate by approximately 85%.
The Role of Artificial Intelligence in Enhancing Organizational Procurement Productivity: A Systematic Literature Review Muin, Oskar Ezra Alan; Pandjaitan, M.M. Lanny W.; Lukas, Lukas
Jurnal Indonesia Sosial Teknologi Vol. 7 No. 4 (2026): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v7i4.9189

Abstract

Digital transformation is driving significant changes in various organizational functions, including in the procurement process. Traditional procurement processes  still often rely on manual activities and limited data analysis that can hinder organizational productivity. This study aims to analyze the role  of Artificial Intelligence (AI) in increasing procurement productivity  through the use of data analysis and procurement process automation. The research method used is a literature study by examining various studies related to the implementation of AI in Procurement and supply chain management published in the period 2020–2026. The results of the study show that AI technology such as machine learning, predictive analytics, and intelligent automation is able to improve the efficiency of the procurement process through predicting  needs, automatic supplier evaluation, and data-based decision-making. AI implementation also contributes to reducing human error, increasing transparency, and accelerating the procurement cycle. Thus, the integration of AI in the Procurement system  can increase organizational productivity and support digital transformation in supply chain management.
Co-Authors Agus Wahyudi Fatimura, Muhrinsyah Fitriyanti, Reno Harlis, Gilang Bagaskara Lukas Lukas Muin, Oskar Ezra Alan