Machine learning approach for energy efficiency in the oil and gas sector / (Record no. 178319)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05729namaa22004331i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | EG-GICUC |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20260219100211.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260209s2025 ua a|||frm||| 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloguing agency | EG-GICUC |
| Language of cataloging | eng |
| Transcribing agency | EG-GICUC |
| Modifying agency | EG-GICUC |
| Description conventions | rda |
| 041 0# - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| Language code of summary or abstract | eng |
| -- | ara |
| 049 ## - Acquisition Source | |
| Acquisition Source | Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 006.31 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.18.11.M.Sc.2025.Mo.M |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Mohamed Salah Abuelhamd, |
| Preparation | preparation. |
| 245 10 - TITLE STATEMENT | |
| Title | Machine learning approach for energy efficiency in the oil and gas sector / |
| Statement of responsibility, etc. | by Mohamed Salah Abuelhamd ; Supervised Prof. Dr. Ammar Mohammed Ammar Mohammed, Dr. Muhammad Mahmoud Mustafa El-Gharbawy. |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | منهجة التعلم الآلي لكفاءة الطاقة فى قطاع النفط والغاز |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 105 Leaves : |
| Other physical details | illustrations ; |
| Dimensions | 30 cm. + |
| Accompanying material | CD. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rda content |
| 337 ## - MEDIA TYPE | |
| Media type term | Unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Sc)-Cairo University, 2025. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 101-105. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | The manufacturing of ammonia is a crucial process in the worldwide <br/>chemical sector. It is an energy-intensive activity that generates substantial CO₂ <br/>emissions, especially from the CO₂ removal unit. <br/>Conventional control techniques frequently fail to optimize energy usage and <br/>CO₂ capture because of the intricate nonlinear dynamics inherent in industrial <br/>operations. <br/>This study presents a machine learning framework aimed at improving the <br/>efficiency and sustainability of CO₂ removal systems in ammonia plants that <br/>employ Hot Potassium Carbonate (HPC) solvent technology. <br/> The study implemented and assessed various machine learning models, <br/>such as Gradient Boosting, Artificial Neural Networks (ANN) and XGBoost <br/>utilizing real-time operating data from an industrial ammonia plant in Egypt. <br/>The models were developed to forecast essential process parameters including <br/>steam generation flow rates, CO₂ absorption efficiency and energy consumption <br/>metrics. <br/> The ANN and XGBoost models attained exceptional predictive accuracy, <br/>with R² scores reaching 0.99 for energy-related forecasts and exceeding 0.82 for <br/>CO₂ capture predictions. <br/> The implementation of the AI-driven control method yielded significant <br/>operational enhancements: steam consumption decreased from 12.4 ton/h to <br/>10.1 ton/h, resulting in an 18.5% energy savings while CO₂ capture efficiency <br/>rose from 88.2% to 94.7%, indicating a 6.5% improvement. <br/>These results were corroborated by actual plant measurements, affirming the <br/>models' robustness in genuine industrial environments. <br/> The thesis presents a dependable AI-driven decision support system that <br/>enhances energy efficiency, operational excellence and environmental <br/>sustainability. <br/><br/><br/>ii <br/><br/> <br/><br/>This approach provides a pragmatic answer for enterprises seeking to comply <br/>with global decarbonization objectives by optimizing CO₂ capture and <br/>minimizing energy use. <br/>The thesis underscores the extensive application of machine learning in processing <br/>industries and establishes a basis for future endeavors that <br/>incorporate deep learning, real-time IoT data, and predictive maintenance. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | يُعد إنتاج الأمونيا عملية حيوية في قطاع الصناعات الكيميائية على مستوى العالم. ومع ذلك، فهي عملية كثيفة الاستهلاك للطاقة وتنتج كميات كبيرة من انبعاثات ثاني أكسيد الكربون (CO₂)، خاصة من وحدة إزالة ثاني أكسيد الكربون. غالبًا ما تفشل تقنيات التحكم التقليدية في تحسين استهلاك الطاقة وكفاءة التقاط ثاني أكسيد الكربون بسبب التعقيدات غير الخطية الكامنة في العمليات الصناعية.<br/> يقدم هذا البحث إطارًا قائمًا على تقنيات تعلم الآلة يهدف إلى تحسين كفاءة واستدامة أنظمة إزالة ثاني أكسيد الكربون في مصانع الأمونيا التي تعتمد على تقنية مذيب بيكربونات البوتاسيوم الساخن (HPC).<br/> تم في هذه الدراسة تطبيق وتقييم العديد من نماذج تعلم الآلة، مثل Gradient Boosting والشبكات العصبية الاصطناعية (ANN) وXGBoost وذلك باستخدام بيانات تشغيلية لحظية حقيقية مأخوذة من مصنع أمونيا صناعي في مصر. تم تطوير النماذج لتوقع معلمات تشغيل أساسية تشمل معدلات تدفق توليد البخار، كفاءة امتصاص ثاني أكسيد الكربون، ومؤشرات استهلاك الطاقة. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Issues CD | Issues also as CD. |
| 546 ## - LANGUAGE NOTE | |
| Text Language | Text in English and abstract in Arabic & English. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine Learning |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | التعلم الآلي |
| 653 #1 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Machine Learning |
| -- | Ammonia Production |
| -- | CO2 Removal |
| -- | Energy Sustainability |
| -- | Operational Resilience |
| -- | Artificial Neural Networks |
| -- | Industrial Process Optimization |
| -- | تعلم الآلة |
| -- | إنتاج الأمونيا |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ammar Mohammed Ammar Mohammed |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Muhammad Mahmoud Mustafa El-Gharbawy |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2025 |
| Supervisory body | Ammar Mohammed Ammar Mohammed |
| -- | Muhammad Mahmoud Mustafa El-Gharbawy |
| Universities | Cairo University |
| Faculties | Faculty of Graduate Studies for Statistical Research |
| Department | Department of Data Science |
| 905 ## - Cataloger and Reviser Names | |
| Cataloger Name | Shimaa |
| Reviser Names | Eman Ghareb |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| Edition | 21 |
| Suppress in OPAC | No |
| Source of classification or shelving scheme | Home library | Current library | Date acquired | Inventory number | Full call number | Barcode | Date last seen | Effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | قاعة الرسائل الجامعية - الدور الاول | 09.02.2026 | 93327 | Cai01.18.11.M.Sc.2025.Mo.M | 01010110093327000 | 09.02.2026 | 09.02.2026 | Thesis |