18074827. FORECASTING ENERGY DEMAND AND CO2 EMISSIONS FOR A GAS PROCESSING PLANT INTEGRATED WITH POWER GENERATION FACILITIES simplified abstract (SAUDI ARABIAN OIL COMPANY)
Contents
- 1 FORECASTING ENERGY DEMAND AND CO2 EMISSIONS FOR A GAS PROCESSING PLANT INTEGRATED WITH POWER GENERATION FACILITIES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FORECASTING ENERGY DEMAND AND CO2 EMISSIONS FOR A GAS PROCESSING PLANT INTEGRATED WITH POWER GENERATION FACILITIES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Energy Efficiency and Emissions Monitoring Technology
- 1.13 Original Abstract Submitted
FORECASTING ENERGY DEMAND AND CO2 EMISSIONS FOR A GAS PROCESSING PLANT INTEGRATED WITH POWER GENERATION FACILITIES
Organization Name
Inventor(s)
Mussa Hadi Alamri of Dammam (SA)
Muhammad Abbas of Dhahran (SA)
Ali H. Al-qahtani of Dammam (SA)
FORECASTING ENERGY DEMAND AND CO2 EMISSIONS FOR A GAS PROCESSING PLANT INTEGRATED WITH POWER GENERATION FACILITIES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18074827 titled 'FORECASTING ENERGY DEMAND AND CO2 EMISSIONS FOR A GAS PROCESSING PLANT INTEGRATED WITH POWER GENERATION FACILITIES
Simplified Explanation
The patent application describes a computer-implemented method for determining energy efficiency and emissions in a gas processing plant using real-time energy stream data and machine learning algorithms.
- Correlations of energy streams between demand-side energy demands and supply-side fuel requirements are generated.
- Machine learning algorithms are trained to identify relationships among energy consumers and energy sources.
- Forecasted values of total energy consumption are determined using the machine learning algorithms and real-time data.
- Forecasting models are re-trained if there is a significant error between forecasted and actual energy demand.
- An energy intensity (EI) for the plant is generated, along with determining CO2 emissions.
Key Features and Innovation
- Real-time energy stream data analysis for energy efficiency and emissions determination.
- Correlation of energy streams between demand-side and supply-side energy requirements.
- Machine learning algorithms for identifying relationships among energy consumers and sources.
- Forecasting models for predicting total energy consumption.
- Re-training of forecasting models based on actual energy demand.
- Generation of energy intensity and CO2 emissions data for the gas processing plant.
Potential Applications
This technology can be applied in various industries where energy efficiency and emissions monitoring are crucial, such as manufacturing plants, power plants, and refineries.
Problems Solved
This technology addresses the challenges of accurately predicting and managing energy consumption and emissions in industrial settings, leading to improved efficiency and environmental impact.
Benefits
- Enhanced energy efficiency and reduced emissions.
- Improved forecasting accuracy for energy consumption.
- Real-time monitoring of energy streams for proactive decision-making.
- Cost savings through optimized energy usage.
Commercial Applications
Energy Efficiency and Emissions Monitoring Technology in Gas Processing Plants: Improving Sustainability and Operational Efficiency This technology can be commercialized as a comprehensive solution for gas processing plants to enhance sustainability, reduce operational costs, and comply with environmental regulations.
Prior Art
No prior art information is available at this time.
Frequently Updated Research
There is ongoing research in the field of energy efficiency and emissions monitoring using real-time data and machine learning algorithms to optimize industrial processes.
Questions about Energy Efficiency and Emissions Monitoring Technology
Question 1
How does this technology contribute to reducing greenhouse gas emissions in industrial operations?
Answer: This technology helps in accurately monitoring and managing energy consumption, leading to optimized operations and reduced emissions in industrial settings.
Question 2
What are the potential cost savings associated with implementing this energy efficiency and emissions monitoring technology?
Answer: By improving energy efficiency and reducing waste, this technology can lead to significant cost savings in terms of energy consumption and operational expenses for industrial plants.
Original Abstract Submitted
Systems and methods include a computer-implemented method for determining energy efficiency and emissions. Real-time energy stream data for a gas processing plant is received. Correlations of energy streams between demand-side energy demands for meeting production requirements of the gas processing plant and fuel requirements for supply-side equipment of the gas processing plant are generated. Machine learning algorithms are trained using the correlations of the energy streams to identify relationships among dependent variables and independent variables of demand-side energy consumers and supply-side energy sources. Forecasted values of total energy consumption of the gas processing plant are determined using the machine learning algorithms and real-time energy stream data. Forecasting models are re-trained using new data if an error between the forecasted values and actual energy demand exceeds a threshold. An energy intensity (EI) for the gas processing plant is generated. COemissions for the gas processing plant are determined.
