{"id":3059,"date":"2024-06-19T05:05:45","date_gmt":"2024-06-19T05:05:45","guid":{"rendered":"https:\/\/bhaviklogistics.com\/happisigns\/product\/ai-driven-energy-usage-analytics\/"},"modified":"2024-08-09T12:00:36","modified_gmt":"2024-08-09T12:00:36","slug":"maximise-efficiency-minimise-waste-with-energy-optimisation","status":"publish","type":"product","link":"https:\/\/networkscience.ai\/case-genie\/product\/maximise-efficiency-minimise-waste-with-energy-optimisation\/","title":{"rendered":"Maximise Efficiency & Minimise Waste with Energy Optimisation"},"content":{"rendered":"\n\n\n\n\n
Role<\/th>\nDeep Tech Used<\/th>\nIndustry<\/th>\nPotential Vector<\/th>\nPotential Vector Benefit<\/th>\n<\/tr>\n<\/thead>\n
CEO<\/p>\n

CIO<\/td>\n

Artificial Intelligence (AI)
\nData Insights<\/td>\n		Energy & Utilities<\/td>\nData<\/td>\n36%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

\n

Use Case Description <\/strong><\/h3>\n

Using AI to analyze energy usage patterns optimizes power distribution by predicting demand fluctuations, identifying opportunities for energy conservation, and ensuring efficient resource allocation. This data-driven approach improves grid performance, reduces energy wastage, and enhances overall energy management.<\/p>\n

 <\/p>\n

Case Study: <\/span><\/b>AI-Powered Energy Optimization In Coal-Fired Power Generation<\/strong><\/h2>\n

\u00a0<\/span><\/h2>\n

Challenges<\/b><\/span><\/h3>\n

A coal-fired power plant in India was struggling with inefficiencies in its coal handling processes. Manual operations led to inconsistencies in coal properties such as size, grade mix, and moisture levels. These variations resulted in higher loss on ignition, undetected issues in coal handling plant screens, and increased heat losses in the boilers. The facility needed a real-time monitoring and optimization solution to improve boiler efficiency, reduce unburnt carbon, and lower overall power generation costs.<\/span>\u00a0<\/span><\/p>\n

Solution<\/span><\/b><\/h3>\n

The power plant implemented an AI-based computer vision system to monitor and optimize its coal handling and combustion processes. This technology provided:<\/span>\u00a0<\/span><\/p>\n

    \n
  • Real-time analysis of coal properties (size distribution, moisture, and color mix).<\/span>\u00a0<\/span><\/li>\n
  • Optimization of burden mix components and chemistry factors.<\/span>\u00a0<\/span><\/li>\n
  • Predictive insights on coal and coke mixtures.<\/span>\u00a0<\/span><\/li>\n
  • Resistance set point recommendations for different burden mixes.<\/span>\u00a0<\/span><\/li>\n
  • Automated alerts for significant deviations from optimal parameters.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

    Results\/Benefits<\/span><\/b><\/h3>\n
      \n
    • Achieved over 90% accuracy in grade-mix optimization.<\/span>\u00a0<\/span><\/li>\n
    • Reduced unburnt carbon losses by 1-2%.<\/span>\u00a0<\/span><\/li>\n
    • Lowered overall power generation costs by 5-8%.<\/span>\u00a0<\/span><\/li>\n
    • Decreased specific power consumption by 1%.<\/span>\u00a0<\/span><\/li>\n
    • Reduced unburnt carbon in boilers by 1.1% through proactive actions based on AI-generated alerts.<\/span>\u00a0<\/span><\/li>\n
    • Realized an annualized financial benefit equivalent to approximately $280,000 USD over a six-month period.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

      The AI system’s ability to provide real-time insights enabled the plant operators to make data-driven decisions, leading to more efficient resource utilization and improved boiler performance. By optimizing the coal properties and combustion processes, the power plant significantly enhanced its operational efficiency and reduced its environmental impact.<\/span>\u00a0<\/span><\/p>\n

      \u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

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