ENERGY
Ready to transform your energy operations with end-to-end data transformation?
Pain points in the Energy industry
Unplanned downtime impacting production
How Acuvate’s Digital Services Framework Helps
Improve asset availability through forecasting early fault detection in critical components and subsequently conduct proactive replacements
1. Devices
What Happens: Timeseries data from all critical devices, such as Engines, Turbines, and Compressors as well as maintenance information from ERP will be collected in this business process.
Impact: Ensures access to all necessary data to build the solution.
2. Connectivity
What Happens: The data collected by these devices is transmitted in real-time using the most suitable connectivity technology - 4G, 5G, WIFI, LPWAN, or others - depending on the infrastructure, size and the critical nature of the data.
Impact: Ensures seamless and immediate data flow, also facilitating quick response to emerging issues before they escalate into downtime.
3. Edge Compute
What Happens: Data processing occurs close to source (on-site or near-site), allowing for real-time analysis of health and performance. Together with 5G as transport, this minimizes latency in detecting potential faults.
Impact: Enables immediate identification and analysis of issues, reducing the time to action for maintenance teams.
4.Cloud Compute & Storage
What Happens: Aggregated and processed data from various sources is transferred to a cloud-based or on-prem data platform where it's stored and analyzed. This centralized data management enhances the depth of analysis and keeps data ownership crystal clear.
Impact: Facilitates comprehensive analytics for predictive maintenance, identifying trends that indicate potential failures.
5. Applications & Services
What Happens: Advanced AI-ML models analyze the cloud-stored data to predict early equipment failure and recommend maintenance activities. These applications turn data from various sources into actionable insights.
Impact: Transforms preventive maintenance into predictive maintenance, scheduling repairs before equipment fails.
6. Inform Decision Makers
What Happens: Decision-makers receive actionable intelligence through various channels such as email, digital twins, and direct alerts. This information includes predictive maintenance schedules, risk assessments, and recommended actions.
Impact: Ensures that decision-makers have all the necessary information to approve maintenance actions swiftly, reducing the risk of unplanned downtime and improving efficiency.
7. Support Decision Making
What Happens: The system generates detailed reports and alerts based on AI-ML analysis as input for Digital Twin application, suggesting specific components for inspection or replacement. These recommendations are prioritized based on the severity and impact of potential failures. Gen AI and XR applications provide seamless interface and deep scenario understanding.
Impact: Maintenance teams can make informed decisions quickly, focusing on the most critical maintenance tasks first.
Leakages leading to safety hazards and negative environmental impacts
How Acuvate’s Digital Services Framework Helps
Real-time transfers of high-definition imagery data and asset maintenance data can help identify potential leaks / emissions in the infrastructure
1. Devices
What Happens: Sensors, including high-definition cameras and environmental monitoring IoT devices, are deployed throughout the energy facility. These devices are designed to detect changes in environmental conditions and capture imagery indicating leaks or emissions. Dependent on the facility, these cameras could be fixed on robots or drones. Data on planned activities is also collected from ERP systems to identify the root cause of the issue.
Impact: Enables comprehensive monitoring of the facility for early detection of leakages, contributing to a safer and more environmentally friendly operation.
2. Connectivity
What Happens: The collected imagery and environmental data are transmitted in real-time, using high-speed connectivity options like 5G, to ensure timely data delivery for immediate AI-MV analysis, where needed.
Impact: Facilitates the rapid transfer of large volumes of high-definition data, allowing for swift detection and response to potential leakages.
3. Edge Compute
What Happens: If a time-sensitive use case, real-time AI-MV processing of the data, including imagery analysis and environmental readings, occurs at or near the data collection point. This step identifies anomalies indicative of leaks or emissions in real time. Based on the outcome, immediate action is taken, such as setpoints back to operational teams.
Impact: Minimizes latency in data processing, enabling faster identification of potential safety or environmental issues.
4.Cloud Compute & Storage
What Happens: Data is further aggregated in cloud-based or on-prem Data Platform where all enterprise data wide will be stored and analyzed. Here, advanced AI and machine learning algorithms assess the severity and pinpoint the location of detected leaks or emissions.
Impact: Enhances decision-making with deep analysis, storing historical data to improve predictive accuracy over time.
5. Applications & Services
What Happens: AI-MV technologies sift through the data, identifying and confirming potential leaks or emissions. These technologies also help prioritize response actions based on the detected risks.
Impact: Transforms data into actionable insights, enabling targeted interventions to mitigate leakages and their impacts.
6. Inform Decision Makers
What Happens: Decision-makers receive concise, actionable reports detailing the location, severity, and recommended responses to the detected leaks or emissions, through emails, alerts, and digital dashboards.
Impact: Ensures that decision-makers are well-informed to swiftly authorize and prioritize repair actions, minimizing safety hazards and environmental impacts.
7. Support Decision Making
What Happens: The system generates and delivers insights and recommendations for addressing detected leaks or emissions, including suggested maintenance or repair actions, to relevant operational teams. Gen AI, XR, Power BI or email tooling is used to deliver crystal clear message to the decision maker.
Impact: Equips maintenance and safety teams with the necessary information to decide on the most effective and immediate corrective actions.
Get in touch with an Acuvate Advisor today!
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Inability to move towards autonomous (i.e., reduced staff) plants
How Acuvate’s Digital Services Framework Helps
Obtaining from a broad range of sensors real-time transfers of data enables AI real-time decision making to drive autonomous operations
1. Devices
What Happens: Deployment of a diverse array of sensors, IoT devices and other data sources like software applications throughout the plant to monitor operations, performance, environmental conditions, and safety parameters in real time.
Impact: Provides a comprehensive data collection network that forms the foundation for autonomous operations, enabling machines and systems to operate independently based on real-time data.
2. Connectivity
What Happens: Utilization of advanced connectivity options (such as 5G, or other suitable technologies) ensures seamless, high-speed transmission of collected data for immediate processing and action.
Impact: Guarantees that data from sensors and devices is rapidly communicated to processing units, facilitating timely decision-making essential for autonomous operations.
3. Edge Compute
What Happens: When needed, AI-ML-MV based data processing and analysis happen close to the source, allowing for immediate actions such as adjustments in operations, machine behavior, and safety protocols without central command input. 5G is used to support low latency in time-critical issues.
Impact:Reduces latency significantly, enabling real-time responses crucial for the autonomy of plant operations and immediate adaptation to changing conditions.
4.Cloud Compute & Storage
What Happens: All data is stored in a single enterprise wide cloud-based or on-prem Data Platform to avoid isolated data streams. Further analysis of data happens in the cloud, leveraging more powerful AI- ML-MV algorithms to optimize operations, predict maintenance needs, and refine autonomous processes over time.
Impact: Enhances long-term operational efficiency and reliability of autonomous systems by learning from vast amounts of data and improving decision-making algorithms.
5. Applications & Services
What Happens: Advanced AI applications process the data to make complex decisions autonomously, scheduling maintenance, and optimizing production lines without human intervention. Increased decision making at this step can result in creating set points going back to operations for them to execute and over time to execute direct. Robots and drones can be utilized to execute set points.
Impact: Enables the transformation of data into actionable intelligence, facilitating autonomous plant operations and reducing the need for direct human oversight.
6. Inform Decision Makers
What Happens: Decision-makers receive updates and reports on autonomous operations, including performance metrics, potential issues, and AI-generated suggestions for further optimization.
Impact:Keeps human decision-makers in the loop with actionable insights, allowing them to oversee autonomous operations effectively and intervene strategically when necessary.
7. Support Decision Making
What Happens: AI systems provide recommendations and automated decisions for operational adjustments, maintenance schedules, and safety measures, all based on real-time data analysis. Tools used include business applications executing actions while keeping the decision maker updated on these decisions and actions.
Impact: Empowers the plant to operate autonomously by making informed decisions on-the-fly, minimizing downtime, and enhancing productivity.
Siloed data leads to operational inefficiencies
How Acuvate’s Digital Services Framework Helps
Large amounts of data can be transferred from various data sources ensuring accurate, aligned and timely information
1. Devices
What Happens: Sensors, including high-definition cameras and environmental monitoring IoT devices, are deployed throughout the energy facility. These devices are designed to detect changes in environmental conditions and capture imagery indicating leaks or emissions. Dependent on the facility, these cameras could be fixed on robots or drones. Data on planned activities is also collected from ERP systems to identify the root cause of the issue.
Impact: Enables comprehensive monitoring of the facility for early detection of leakages, contributing to a safer and more environmentally friendly operation.
2. Connectivity
What Happens: The collected imagery and environmental data are transmitted in real-time, using high-speed connectivity options like 5G, to ensure timely data delivery for immediate AI-MV analysis, where needed.
Impact: Facilitates the rapid transfer of large volumes of high-definition data, allowing for swift detection and response to potential leakages.
3. Edge Compute
What Happens: If a time-sensitive use case, real-time AI-MV processing of the data, including imagery analysis and environmental readings, occurs at or near the data collection point. This step identifies anomalies indicative of leaks or emissions in real time. Based on the outcome, immediate action is taken, such as setpoints back to operational teams.
Impact: Minimizes latency in data processing, enabling faster identification of potential safety or environmental issues.
4.Cloud Compute & Storage
What Happens: Data is further aggregated in cloud-based or on-prem Data Platform where all enterprise data wide will be stored and analyzed. Here, advanced AI and machine learning algorithms assess the severity and pinpoint the location of detected leaks or emissions.
Impact: Enhances decision-making with deep analysis, storing historical data to improve predictive accuracy over time.
5. Applications & Services
What Happens: AI-MV technologies sift through the data, identifying and confirming potential leaks or emissions. These technologies also help prioritize response actions based on the detected risks.
Impact: Transforms data into actionable insights, enabling targeted interventions to mitigate leakages and their impacts.
6. Inform Decision Makers
What Happens: Decision-makers receive concise, actionable reports detailing the location, severity, and recommended responses to the detected leaks or emissions, through emails, alerts, and digital dashboards.
Impact: Ensures that decision-makers are well-informed to swiftly authorize and prioritize repair actions, minimizing safety hazards and environmental impacts.
7. Support Decision Making
What Happens: The system generates and delivers insights and recommendations for addressing detected leaks or emissions, including suggested maintenance or repair actions, to relevant operational teams. Gen AI, XR, Power BI or email tooling is used to deliver crystal clear message to the decision maker.
Impact: Equips maintenance and safety teams with the necessary information to decide on the most effective and immediate corrective actions.
Contact an Acuvate Advisor today!
Talk to Advisors
Reducing number of Health, Safety, Security and Environmental (HSSE) incidents
How Acuvate’s Digital Services Framework Helps
Automated camera sensors around the plant can track, manage and predict potential occupational hazards in real-time and ensure compliance
1. Devices
What Happens: Automated camera sensors and other safety-specific IoT devices are deployed throughout the facility to continuously monitor for potential hazards, compliance breaches, and unsafe conditions.
Impact: Provides a comprehensive, real-time monitoring system that enhances the ability to detect and respond to potential HSSE incidents before they occur.
2. Connectivity
What Happens: These devices use high-speed connectivity (4G, 5G, among other advanced technologies) to transmit real-time data and imagery to processing centers for immediate analysis, ensuring no delay in hazard detection.
Impact: Facilitates the swift transfer of critical safety data, enabling real-time responses to potential safety issues and enhancing overall safety protocols.
3. Edge Compute
What Happens: Where time critical, edge-based data processing occurs at or near the collection point to quickly identify potential hazards, compliance issues, or environmental risks, minimizing the time to action.
Impact:Reduces latency in hazard recognition and compliance monitoring, allowing for immediate corrective measures to prevent incidents.
4.Cloud Compute & Storage
What Happens: Data and imagery are sent to the integrated on-prem or cloud-based Data Platform for more complex analysis, long-term tracking, and trend analysis, contributing to predictive safety and environmental management strategies.
Impact: Enhances HSSE incident prevention through deep learning and AI analysis, identifying patterns that may predict future incidents and guiding preventive measures.
5. Applications & Services
What Happens: Specialized AI-ML, and computer vision (MV) algorithms analyze the data to detect anomalies, predict potential incidents, and recommend preventive actions such as real time staff alerts. These applications can also ensure that operations are compliant with relevant HSSE regulations. This can be extended in several areas such as safety gear being utilized, alerts to not walk under lifting goods and much more.
Impact: Transforms surveillance data into actionable insights for real-time hazard management and compliance assurance, significantly reducing potential HSSE incidents.
6. Inform Decision Makers
What Happens: Decision-makers receive concise, actionable intelligence on HSSE status, including real-time alerts, compliance reports, and predictive analytics on potential hazards, directly influencing strategic planning and resource allocation.
Impact: Ensures that leadership is fully informed about the HSSE landscape, facilitating proactive measures to enhance safety, security, and environmental stewardship across the plant.
7. Support Decision Making
What Happens: The system generates automated alerts and detailed reports on identified risks, compliance issues, and recommended preventive actions, supporting operational and HSSE teams in making informed decisions. Tools include includes Gen-AI for reporting and analysis purposes, digital twins and XR for real time and realistic visibility into issues, among others.
Impact: Empowers teams with data-driven insights to swiftly address potential safety hazards, ensure compliance, and implement strategic changes to prevent future incidents.
Inability to manage, integrate and optimize data from various energy data sources
How Acuvate’s Digital Services Framework Helps
5G enabled AI can process vast amounts of information from all energy data sources in a unified structure enabling efficient management
1. Devices
What Happens: Deployment of a wide array of sensors, meters, and IoT devices across various points in the energy supply chain to collect diverse data types, including consumption and environmental impact data. Data is also collected from other sources such as ERP and other software applications, weather forecasting tools, alternative energy sources such as wind and solar.
Impact: Establishes a foundational layer for comprehensive data collection, ensuring that all relevant energy data sources are captured for further analysis.
2. Connectivity
What Happens: Utilizes 5G connectivity to ensure the real-time, high-speed transmission of collected data from disparate sources to central processing units or cloud infrastructure, minimizing delays.
Impact: Facilitates the rapid and reliable transfer of large data volumes, crucial for real-time analysis and decision-making in energy management.
3. Edge Compute
What Happens: Where critical, initial processing and AI-ML-MV analysis of data occurs at or near the data source to quickly identify trends, anomalies, or efficiency opportunities. Operators can take immediate action. Set points are sent back to some of the sources of the data if needed.
Impact: Allows for immediate insights and actions at the edge of the network, enhancing responsiveness to changes in energy production or consumption.
4.Cloud Compute & Storage
What Happens: Data is aggregated in a cloud-based or on-prem enterprise Data Platform where all data is stored. Here, advanced AI algorithms process and analyze information from all energy data sources, integrating and optimizing it within a unified structure.
Impact: Provides a centralized, scalable environment for comprehensive data analysis, facilitating the management and optimization of energy resources across diverse sources.
5. Applications & Services
What Happens: AI-ML-MV and other analytics applications utilize the processed data to generate insights, forecasts, and recommendations for optimizing energy production, distribution, and consumption. Given the wide difference in energy sources (gas/wind/solar/oil/ etc.), this step also enables costs, emissions and efficiency optimization.
Impact: Transforms integrated data into actionable intelligence, enabling energy operators to make informed decisions that improve efficiency and sustainability.
6. Inform Decision Makers
What Happens: Decision-makers receive comprehensive reports and dashboards that offer a unified view of energy data, performance metrics, and AI-generated insights, facilitating strategic planning and monitoring.
Impact: Ensures that executives and operational leaders have a holistic understanding of energy operations, enabling them to make informed, strategic decisions that enhance efficiency and sustainability.
7. Support Decision Making
What Happens: The system delivers specific, actionable recommendations to operational teams and decision-makers, highlighting areas for improvement, potential savings, and investment opportunities in the energy sector. Gen-AI tooling addresses the diversity and complexity of energy sources, allowing conversational insights for operators and decision makers.
Impact: Empowers decision-makers with the information needed to optimize energy management practices, adjust strategies, and prioritize investments based on data-driven insights.