Global Lighthouse Network 2026

SOCAR Carbamide’s no-code platform for dynamic energy optimization FIGURE 13 Custom plant configuration enabled by pre-defined equipment that can be dragged and droppedRecommendations tracked and implemented with tailor ed management systemFlow Line Flow: MP Optimize: True Initial: 4.3 t/h Flow Limit: 0-20 t/h Optimized: 0 t/h Boiler A Status: Active Optimize: Inactive Capacity: 32.5 - 130 t/h Decision V ar: YesBoiler C Status: Active Optimize: Inactive Capacity: 32.5 - 130 t/h Decision V ar: YesBoiler B Status: Active Optimize: Active Capacity: 32.5 - 130 t/h Decision V ar: YesEquipment characteristics and sensor -level energy calculations defined by user fr om contr ol panel No-code configuration and simulation platform1 Medium pressure headerHP MP letdown Decision V ar: No58 nodes, 70 flows Flow Line Flow: MP Optimize: True Initial: 4.3 t/h Flow Limit: 0-20 t/h Optimized: 0 t/h Flow Line Flow: MP Optimize: True Initial: 56.53 t/h Flow Limit: 0-130 t/h Optimized: 0 t/hFlow Line Flow: MP Optimize: True Initial: 56.47 t/h Flow Limit: 0-130 t/h Optimized: 47.93 t/hFlow Line Flow: MP Optimize: True Initial: 56.99 t/h Flow Limit: 0-130 t/h Optimized: 130 t/h Equipment and header Drag and drop equipment to the configuratorProduct overview List of available and developing models Model configurator Adjust constraints, decision variables and parameters Results dashboar d Model r esult overview for select model and configuration Recommendations Power ed recommendations and insights Profile Personal information Settings App pr eferencesSimulator Run simulations and scenariosLow-code plant configurator Plant configuration playgr ound Turbine generator Steam to electricity Electricity header Electricity distribution Grid Electrical grid connectionHigh pressure headerNAVIGATION In-house developmentDynamic energy optimization with ML-enabled no-code platform Gray ur ea pr oduction is emission-intensive, driven by natural gas in power and steam generation. To achieve net-zer o commitment and r educe costs, an energy network enabled by ML optimizes power and steam supply by analysing 156 IoT sensors and adjusting 35 parameters closed-loop and in r eal time. Operators use a no-code platform to r e-configur e solutions, run what-if simulations and scale the model to other sites.-18% ygrenE consumption-19% retaW consumption-22% 1 epocS emissionsSOCAR Sumqayit, Azerbaijan Electricity source Electricity sources and distribution Steam sour ce Steam generation and distribution Note: 1. All datapoints in this figure are illustrative only. Source: Global Lighthouse Network. Global Lighthouse Network: Rewiring Operations for Resilience and Impact at Scale 23