The Experts below are selected from a list of 13353 Experts worldwide ranked by ideXlab platform
Mangestiyono Wiji - One of the best experts on this subject based on the ideXlab platform.
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OPTIMASI HEAT EXCHANGER TIPE PLATE CHEVRON DENGAN PEMBERSIHAN KERAK METODE CHEMICAL SPRAY PADA PLTU INDRAMAYU ( OPTIMIZATION OF CHEVRON PLATE TYPE HEAT EXCHANGER USING CHEMICAL SPRAY SCALE CLEANING METHOD IN PLTU INDRAMAYU )
2015Co-Authors: Haqni, Firdaus Kurniawan, Mangestiyono WijiAbstract:Plate heat exchanger (PHE) merupakan alat penukar kalor yang berfungsi untuk mendingin kan air demin yang disirkulasikan untuk mendinginkan lube oil pada sistem PLTU. PHE pada PLTU Indramayu telah digunakan sejak 2011 dan mengalami penurunan efisiensi karena terjadi pengotoran. Melihat pentingnya peran PHE, maka dilakukanlah analisa mengenai efisiensi PHE ketika kondisi kotor dan kondisi setelah dibersihkan. Sehingga akan diketahui mengenai penyabab pengotoran adalah fouling dan scaling. Data yang diambil adalah berdasarkan Data yang berada di central control room. Pengambilan Data yang dibutuhkan adalah temperatur masuk dan keluar dari fluida closed cooling dan open cooling. Dimana pengambilan Data dilakukan selama 20 hari. Sehingga dapat dibandingkan hasil efisiensi alat ketika kotor dan ketika bersih. Maka dalam pemeliharaan alat PHE dapat dilakukan secara tepat. Dari hasil perhitungan, nilai efektifitas alat ketika kondisi kotor pada awalnya yaitu sebesar 61,05% dan menurun sampai 49,81%. Namun setelah dilakukan pembersihan kerak dengan metode chemical spray, maka didapatkan nilai efektifiasnya meningkat kembali menjadi sebesar 62,95%. Dan dari Data alat kondisi bersih hingga pengujian ke – 10, nilai efektifitasnya sebesar 64,89%. Hal tersebut menunjukkan bahwa pembersihan dengan chemical spray sangat berpengaruh terhadap suhu keluaran fluida dari plateh heat exchanger, sehingga berimbas kepada efektifitas alat tersebut. Kata Kunci : Plate Heat Exchanger, Efektifitas, Fouling, Scaling Plate heat exchanger (PHE) is a heat exchanger that serves to cool the demin water is circulated to cool the lube oil plant system. PHE in Indramayu power plant has been used since 2011 and decreased efficiency due to fouling. Seeing the importance of the role of PHE, we conducted an analysis of the efficiency of PHE when dirty condition and the condition after cleaning. So they will know about the different causes of fouling are fouling and scaling. The Data taken is based on Data that is in the central control room. Retrieval of Data required is the temperature in and out of the closed cooling fluid and open cooling. Where the Data collection is done for 20 days. So it can be compared to the results of the efficiency of the tool when it is dirty and when the net. So in PHE equipment maintenance can be done accurately. From the calculation, the value of the effectiveness of the tool when the filthy conditions at first in the amount of 61.05% and decreased to 49.81%. But after scaling with chemical spray method, then the value obtained by efektifiasnya rose again to 62.95%. And from the Data Appliance clean condition to test for - 10, the value of effectiveness of 64.89%. It shows that cleaning with chemical spray affects the fluid outlet temperature of plateh heat exchanger, so that the impact to the effectiveness of these tools. Keyword : Plate Heat Exchanger, Effectiveness, Fouling, Scalin
Haqni, Firdaus Kurniawan - One of the best experts on this subject based on the ideXlab platform.
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OPTIMASI HEAT EXCHANGER TIPE PLATE CHEVRON DENGAN PEMBERSIHAN KERAK METODE CHEMICAL SPRAY PADA PLTU INDRAMAYU ( OPTIMIZATION OF CHEVRON PLATE TYPE HEAT EXCHANGER USING CHEMICAL SPRAY SCALE CLEANING METHOD IN PLTU INDRAMAYU )
2015Co-Authors: Haqni, Firdaus Kurniawan, Mangestiyono WijiAbstract:Plate heat exchanger (PHE) merupakan alat penukar kalor yang berfungsi untuk mendingin kan air demin yang disirkulasikan untuk mendinginkan lube oil pada sistem PLTU. PHE pada PLTU Indramayu telah digunakan sejak 2011 dan mengalami penurunan efisiensi karena terjadi pengotoran. Melihat pentingnya peran PHE, maka dilakukanlah analisa mengenai efisiensi PHE ketika kondisi kotor dan kondisi setelah dibersihkan. Sehingga akan diketahui mengenai penyabab pengotoran adalah fouling dan scaling. Data yang diambil adalah berdasarkan Data yang berada di central control room. Pengambilan Data yang dibutuhkan adalah temperatur masuk dan keluar dari fluida closed cooling dan open cooling. Dimana pengambilan Data dilakukan selama 20 hari. Sehingga dapat dibandingkan hasil efisiensi alat ketika kotor dan ketika bersih. Maka dalam pemeliharaan alat PHE dapat dilakukan secara tepat. Dari hasil perhitungan, nilai efektifitas alat ketika kondisi kotor pada awalnya yaitu sebesar 61,05% dan menurun sampai 49,81%. Namun setelah dilakukan pembersihan kerak dengan metode chemical spray, maka didapatkan nilai efektifiasnya meningkat kembali menjadi sebesar 62,95%. Dan dari Data alat kondisi bersih hingga pengujian ke – 10, nilai efektifitasnya sebesar 64,89%. Hal tersebut menunjukkan bahwa pembersihan dengan chemical spray sangat berpengaruh terhadap suhu keluaran fluida dari plateh heat exchanger, sehingga berimbas kepada efektifitas alat tersebut. Kata Kunci : Plate Heat Exchanger, Efektifitas, Fouling, Scaling Plate heat exchanger (PHE) is a heat exchanger that serves to cool the demin water is circulated to cool the lube oil plant system. PHE in Indramayu power plant has been used since 2011 and decreased efficiency due to fouling. Seeing the importance of the role of PHE, we conducted an analysis of the efficiency of PHE when dirty condition and the condition after cleaning. So they will know about the different causes of fouling are fouling and scaling. The Data taken is based on Data that is in the central control room. Retrieval of Data required is the temperature in and out of the closed cooling fluid and open cooling. Where the Data collection is done for 20 days. So it can be compared to the results of the efficiency of the tool when it is dirty and when the net. So in PHE equipment maintenance can be done accurately. From the calculation, the value of the effectiveness of the tool when the filthy conditions at first in the amount of 61.05% and decreased to 49.81%. But after scaling with chemical spray method, then the value obtained by efektifiasnya rose again to 62.95%. And from the Data Appliance clean condition to test for - 10, the value of effectiveness of 64.89%. It shows that cleaning with chemical spray affects the fluid outlet temperature of plateh heat exchanger, so that the impact to the effectiveness of these tools. Keyword : Plate Heat Exchanger, Effectiveness, Fouling, Scalin
Haeng-kon Kim - One of the best experts on this subject based on the ideXlab platform.
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Architecture Design of a Smart Farm System Based on Big Data Appliance Machine Learning
2020 20th International Conference on Computational Science and Its Applications (ICCSA), 2020Co-Authors: Symphorien Karl Yoki Donzia, Haeng-kon KimAbstract:The size of the world's population increased at a Revolution. The modern expansion of human numbers started but environmental degradation with lack of urban services. To satisfy the growing of human food, worldwide demand for grain the area under production should be increased, and productivity must be improved on yields area firstly. To evaluate the Smart Farming sub-use cases' overall outcome, each economic and environmental benefits, social aspects, and the technical evolution path were evaluated. We have like an significant improvement in the economic outcome of the farm. This paper proposed an implementation of BMS (Big Data Application Machine Learning-based Smart Farm System) with an emphasis on crop productivity and the importance of farmers' income increase. Increasing crop productivity is also important to increase essentials' income, enhance farmer field-level insights, and actionable knowledge to produce when the crop is of the best quality or selling it with a good price. Therefore, in the Smart Farm system proposed in this paper specially in case of big Data science, we need to consider Data analysis and machine learning as the most important steps and then we can include the value of big Data science. Machine learning is an essential ability to learn from Data and provide Data-driven information, decisions, and forecasts. Traditional approaches to machine learning were developed in a different era, like the Data set that fully integrates memory. In addition to the characteristics of Big Data, they create obstacles to traditional techniques. One of the objectives of this document is to summarize the challenges of machine learning with Big Data.
K. Laker - One of the best experts on this subject based on the ideXlab platform.
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oracle big Data handbook
2013Co-Authors: Tom Plunkett, Robert Stackowiak, Bruce Nelson, Mark Hornick, Khader Mohiuddin, Debra Harding, Gokula Mishra, Helen Sun, Brian Macdonald, K. LakerAbstract:Transform Big Data into Insight "In this book, some of Oracle's best engineers and architects explain how you can make use of big Data. They'll tell you how you can integrate your existing Oracle solutions with big Data systems, using each where appropriate and moving Data between them as needed." -- Doug Cutting, co-creator of Apache Hadoop Cowritten by members of Oracle's big Data team, Oracle Big Data Handbook provides complete coverage of Oracle's comprehensive, integrated set of products for acquiring, organizing, analyzing, and leveraging unstructured Data. The book discusses the strategies and technologies essential for a successful big Data implementation, including Apache Hadoop, Oracle Big Data Appliance, Oracle Big Data Connectors, Oracle NoSQL Database, Oracle Endeca, Oracle Advanced Analytics, and Oracle's open source R offerings. Best practices for migrating from legacy systems and integrating existing Data warehousing and analytics solutions into an enterprise big Data infrastructure are also included in this Oracle Press guide. Understand the value of a comprehensive big Data strategy Maximize the distributed processing power of the Apache Hadoop platform Discover the advantages of using Oracle Big Data Appliance as an engineered system for Hadoop and Oracle NoSQL Database Configure, deploy, and monitor Hadoop and Oracle NoSQL Database using Oracle Big Data Appliance Integrate your existing Data warehousing and analytics infrastructure into a big Data architecture Share Data among Hadoop and relational Databases using Oracle Big Data Connectors Understand how Oracle NoSQL Database integrates into the Oracle Big Data architecture Deliver faster time to value using in-Database analytics Analyze Data with Oracle Advanced Analytics (Oracle R Enterprise and Oracle Data Mining), Oracle R Distribution, ROracle, and Oracle R Connector for Hadoop Analyze disparate Data with Oracle Endeca Information Discovery Plan and implement a big Data governance strategy and develop an architecture and roadmap
Symphorien Karl Yoki Donzia - One of the best experts on this subject based on the ideXlab platform.
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Architecture Design of a Smart Farm System Based on Big Data Appliance Machine Learning
2020 20th International Conference on Computational Science and Its Applications (ICCSA), 2020Co-Authors: Symphorien Karl Yoki Donzia, Haeng-kon KimAbstract:The size of the world's population increased at a Revolution. The modern expansion of human numbers started but environmental degradation with lack of urban services. To satisfy the growing of human food, worldwide demand for grain the area under production should be increased, and productivity must be improved on yields area firstly. To evaluate the Smart Farming sub-use cases' overall outcome, each economic and environmental benefits, social aspects, and the technical evolution path were evaluated. We have like an significant improvement in the economic outcome of the farm. This paper proposed an implementation of BMS (Big Data Application Machine Learning-based Smart Farm System) with an emphasis on crop productivity and the importance of farmers' income increase. Increasing crop productivity is also important to increase essentials' income, enhance farmer field-level insights, and actionable knowledge to produce when the crop is of the best quality or selling it with a good price. Therefore, in the Smart Farm system proposed in this paper specially in case of big Data science, we need to consider Data analysis and machine learning as the most important steps and then we can include the value of big Data science. Machine learning is an essential ability to learn from Data and provide Data-driven information, decisions, and forecasts. Traditional approaches to machine learning were developed in a different era, like the Data set that fully integrates memory. In addition to the characteristics of Big Data, they create obstacles to traditional techniques. One of the objectives of this document is to summarize the challenges of machine learning with Big Data.
