SEMS Technologies LLC · Memphis, Tennessee

AI Energy Optimization
for Industrial Facilities

SEMS Technologies LLC develops AI-driven decision-support systems that optimize industrial energy consumption using SCADA data, machine learning, and digital twin simulation.

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22–25%
Reduction in electricity consumption
Modeled validation result
$18K
Daily cost savings per facility
Facility-dependent estimate
200t
CO₂ reduction potential per day
Modeled validation result
What we do

Advanced AI energy optimization for industrial operations

SEMS Technologies provides advanced AI-based energy optimization solutions for refineries, petrochemical plants, manufacturing facilities, and industrial operations.

Our Smart Energy Management System (SEMS) analyzes real operational data to generate energy-efficient recommendations while maintaining safety and production constraints. The system operates in advisory mode — supporting human operators with AI-generated insights without disrupting existing control infrastructure.

Key capabilities

Built for the complexity of industrial environments

📈
AI-based energy demand forecasting
Predictive models that learn your facility's load patterns and anticipate energy demand before it occurs.
⚙️
Reinforcement learning optimization
Adaptive algorithms that continuously improve dispatch recommendations based on real operational feedback.
🔗
SCADA / IoT integration
Protocol-agnostic connection to existing industrial control systems — no new hardware or operational disruption.
🧬
Digital twin simulation
High-fidelity facility models that enable safe pre-deployment validation without production risk.
👤
Advisory-mode deployment (human-in-the-loop)
AI recommendations reviewed and approved by your operators before any action — safety-first design.
📊
Real-time performance reporting
Energy consumption, cost savings, and CO₂ metrics tracked and reported continuously.
How it works

Three simple steps from data to savings

A safe, straightforward process designed for industrial environments where continuity and operator control are non-negotiable.

01
Data ingestion
SEMS connects to your existing SCADA, IoT sensors, and metering systems. No new hardware. No operational disruption.
02
AI analysis
Machine learning and reinforcement learning models analyze your operational patterns and identify optimization opportunities.
03
Recommendations
SEMS delivers advisory-mode recommendations to your operators. Your team reviews and approves every action.
Validated results

Measurable impact grounded in real industrial data

Based on modeled validation using authentic historical SCADA and metering data from operating industrial facilities.

22–25%
Reduction in electricity consumption per optimized facility
$12–18K
Daily cost savings (facility-dependent)
200t
CO₂ reduction potential per facility per day
Note on results: All figures are based on simulation and offline validation using real historical industrial SCADA data. Results are not guaranteed and will vary based on facility type, size, baseline efficiency, and operational conditions. Live deployment results may differ.
Industries served

Designed for high-load U.S. industrial facilities

SEMS is purpose-built for sectors where energy costs are significant and where optimization directly impacts both profitability and environmental compliance.

Oil & Gas
Refineries
Petrochemical Plants
Manufacturing
Industrial Processing Facilities
Compressor Stations
Utilities
About the founder
S
Shaker Alamir
Founder & CEO · SEMS Technologies LLC

Shaker Alamir is an energy systems engineer and AI researcher specializing in industrial energy optimization. His work spans oil and gas engineering, energy systems modeling, and applied machine learning for industrial SCADA environments.

His work includes a peer-reviewed SPE conference publication presented in Houston, a U.S. provisional patent (No. 63/949,198) protecting the SEMS platform architecture, and validated research using authentic real industrial SCADA data from three operating facilities. He holds a B.S. in Oil & Gas Engineering and an M.S.-equivalent in Energy Systems Engineering (TEC credential evaluated, No. 002561355).

SPE-227886-MS · Published USPTO No. 63/949,198 M.S. Energy Systems Engineering SCADA · ML · Reinforcement Learning AI TechX · UTK Affiliated
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Currently onboarding pilot partners

We are currently onboarding pilot partners for advisory-mode evaluation. No disruption to operations. No commitment required to apply.

Apply for Pilot Partnership →