DEEP LEARNING - ARTIFICIAL NEURAL NETWORK (ANN) ESTIMATION OF SILICA AND HARDNESS IN WATER TREATMENT PLANT

SAPUTRA, FAJAR BHASKORO CATUR (2025) DEEP LEARNING - ARTIFICIAL NEURAL NETWORK (ANN) ESTIMATION OF SILICA AND HARDNESS IN WATER TREATMENT PLANT. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

In clean water treatment, in general, there are 3 buildings or constructions, namely: Intake, Water Treatment Plant (WTP), and Reservoir. WTP is the main building or construction of a clean water treatment. WTP is a system or facility that functions to treat water from contaminated influent water quality to obtain the desired water quality treatment according to quality standards or ready for consumption. WTP is an important facility around the world that will produce clean and healthy water for consumption. Monitoring water quality by cost-effective methods is important as the aquifers are vulnerable to contamination from the uncontrolled discharge of sew-age, agricultural, and industrial activities. Faulty planning and mismanagement of WTP are the principal reasons for water quality. Application of a reliable estimation model for any WTP is essential in order to provide a tool for predicting influent water quality and to form a basis for controlling the operation of the process. The factors that influence WTP could be represented by the Alkalinity, PH, Silica, and Hardness. Silica (SiO2) is typically found in well water supplies. Most of the silica found in waters is a result of dissolving silica-containing rock. Silica content in brackish water is generally in the range of 20 to 60 ppm. The raw water has a high temperature ranging between 60-70o C, high Hardness, high Silica content, and high salts concentration. So, this water needs cooling and softening to meet membranes specifications, besides desalting to meet drinking water specifications. Whereas, Hardness of water is the measure of water capacity to precipitate soaps. Hardness minerals are calcium and magnesium in parts per million. Water hardness may be either carbonate or non-carbonate. The carbonate hardness is caused by the bicarbonate of calcium and magnesium while non-carbonate hardness is caused by the sulfate and chlorides of calcium and magnesium. Keywords: Artificial Neural Network, Silica, Hardness, Water Treatment Plant

Item Type:	Thesis (S1)
Call Number CD:	JM/INFO. 25 007
NIM/NIDN Creators:	41518010041
Uncontrolled Keywords:	Artificial Neural Network, Silica, Hardness, Water Treatment Plant
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.32 Neural Nets (Neural Network)/Jaringan Saraf Buatan 600 Technology/Teknologi > 630 Agriculture and Related Technologies/Pertanian dan Teknologi Terkait > 631 Specific Techniques; Apparatus, Equipment, Materials/Teknik Spesifik; Peralatan, Peralatan, Bahan > 631.7 Water Conservation/Konservasi Air 600 Technology/Teknologi > 650 Management, Public Relations, Business and Auxiliary Service/Manajemen, Hubungan Masyarakat, Bisnis dan Ilmu yang Berkaitan > 658 General Management/Manajemen Umum > 658.2 Plant Management/Manajemen Pabrik
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	18 Mar 2025 02:08
Last Modified:	18 Mar 2025 02:08
URI:	http://repository.mercubuana.ac.id/id/eprint/94966

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