Masulili, Alwan Naufal (2026) Optimasi Specific Power Consumption Untuk Mereduksi Emisi dan Efisiensi Energi Di LNG Plant Dengan Response Surface Methodology. Masters thesis, Institut Teknologi Sepuluh Nopember.
![[thumbnail of 6032232006-Master_Thesis.pdf]](http://repository.its.ac.id/style/images/fileicons/text.png) |
Text
6032232006-Master_Thesis.pdf Restricted to Repository staff only Download (1MB) | Request a copy |
Abstract
Dalam rangka mendukung target dekarbonisasi nasional sebagaimana tertuang dalam Rencana Umum Energi Nasional (RUEN), peningkatan efisiensi energi pada sektor Liquefied Natural Gas (LNG) menjadi salah satu strategi yang krusial. Proses pencairan gas alam pada fasilitas LNG menghasilkan emisi gas rumah kaca (GRK) yang signifikan, terutama akibat konsumsi energi pada sistem refrigerasi. Salah satu indikator utama efisiensi proses tersebut adalah Specific Power Consumption (SPC), yang merepresentasikan rasio konsumsi energi kompresi terhadap jumlah LNG yang diproduksi. Komposisi Mixed Refrigerant (MR), yang terdiri dari nitrogen (N₂), metana (CH₄), dan etana (C₂H₆), berperan penting dalam menentukan kinerja termodinamika sistem dan nilai SPC. Penelitian ini bertujuan untuk memodelkan dan mengoptimalkan hubungan antara komposisi MR dan SPC menggunakan Response Surface Methodology (RSM). Penelitian dilakukan pada fasilitas LNG PT XYZ dengan memanfaatkan data operasi aktual periode 1 Januari hingga 7 September 2025. Perancangan eksperimen dilakukan menggunakan Box–Behnken Design (BBD) tiga faktor, yang memungkinkan eksplorasi pengaruh non-linear dan interaksi antar variabel dalam ruang desain yang realistis dan sesuai dengan batas operasional pabrik. Hasil analisis menunjukkan bahwa model kuadratik RSM mampu merepresentasikan hubungan antara komposisi MR dan SPC secara signifikan, dengan kinerja yang lebih baik daripada model linear. Optimasi model menghasilkan nilai SPC minimum sebesar 282,65 kWh/ton LNG, dengan rentang ketidakpastian pada tingkat kepercayaan 95% sebesar 279,40–285,90 kWh/ton LNG. Analisis permukaan respon dan eigenvalue mengindikasikan bahwa titik stasioner merupakan maksimum lokal, sehingga kondisi minimasi SPC berada pada batas ruang desain eksperimen. Evaluasi terhadap data baseline SPC tahun 2025 menunjukkan bahwa kondisi optimum tersebut berpotensi memberikan penghematan energi hingga 16,20 kWh/ton LNG, yang selanjutnya dapat diterjemahkan menjadi penghematan biaya operasional dan reduksi emisi GRK secara signifikan, tanpa memerlukan investasi peralatan tambahan. Dengan demikian, penelitian ini memberikan dasar teknis dan ekonomis yang kuat bahwa optimasi komposisi MR berbasis RSM dapat digunakan sebagai alat pendukung keputusan untuk meningkatkan efisiensi energi dan mendukung strategi dekarbonisasi fasilitas LNG.
==================================================================================================================================
In support of Indonesia’s national decarbonization targets as outlined in the National Energy Plan (RUEN), improving energy efficiency in the Liquefied Natural Gas (LNG) sector has become a critical strategy. LNG liquefaction processes are associated with significant greenhouse gas (GHG) emissions, primarily driven by energy consumption in refrigeration systems. One of the key indicators used to evaluate energy efficiency in LNG plants is the Specific Power Consumption (SPC), defined as the ratio of compression power to LNG production rate. The composition of the Mixed Refrigerant (MR), particularly nitrogen (N₂), methane (CH₄), and ethane (C₂H₆), plays a crucial role in determining the thermodynamic performance of the refrigeration system and the resulting SPC. This study aims to model and optimize the relationship between MR composition and SPC using a statistical Response Surface Methodology (RSM) approach. A case study was conducted at the LNG facility operated by PT XYZ using actual operational data from 1 January to 7 September 2025. The experimental design was constructed using a three-factor Box–Behnken Design (BBD), allowing the investigation of non-linear effects and interactions among MR components within a constrained design space that reflects realistic operational limits. The results indicate that a quadratic RSM model provides a significantly better representation of the SPC behavior compared to a first-order model. The optimization yields a minimum predicted SPC of 282.65 kWh/ton LNG, with a 95% confidence interval ranging from 279.40 to 285.90 kWh/ton LNG. Eigenvalue analysis and response surface visualization reveal that the stationary point corresponds to a local maximum, indicating that SPC minimization occurs at the boundary of the experimental region. Comparison with the 2025 baseline SPC performance demonstrates that the optimized MR composition has the potential to deliver energy savings of up to 16.20 kWh/ton LNG. These savings can be directly translated into reduced natural gas consumption, operational cost savings, and significant GHG emission reductions, without requiring additional capital investment or equipment modification. Therefore, this study provides strong technical and economic justification for the use of RSM-based MR composition optimization as a decision-support tool to enhance energy efficiency and support decarbonization initiatives in LNG liquefaction facilities.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Efisiensi Energi, Emisi Gas Rumah Kaca (GRK), Liquefied Natural Gas (LNG), Mixed Refrigerant (MR), Response Surface Methodology (RSM), Specific Power Consumption (SPC), Energy Efficiency, Greenhouse Gas Emissions (GHG) |
| Subjects: | Q Science > QA Mathematics > QA279 Response surfaces (Statistics). Analysis of covariance. T Technology > T Technology (General) > T58.8 Productivity. Efficiency T Technology > TD Environmental technology. Sanitary engineering > TD171.75 Climate change mitigation T Technology > TP Chemical technology > TP155.7 Chemical processes. T Technology > TP Chemical technology > TP761.L5 Liquefied natural gas |
| Divisions: | Interdisciplinary School of Management and Technology (SIMT) > 61101-Master of Technology Management (MMT) |
| Depositing User: | Alwan Naufal Masulili |
| Date Deposited: | 02 Jun 2026 05:52 |
| Last Modified: | 02 Jun 2026 05:52 |
| URI: | http://repository.its.ac.id/id/eprint/133460 |
Actions (login required)
 |
View Item |