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CleanDesign Income

CleanDesign develops hybrid energy management systems that integrate battery energy storage and engine automation to optimize power loads for drilling operations. This technology reduces fuel consumption and emissions while minimizing operational downtime and maintenance costs, resulting in annual savings of approximately $300,000 per rig.

Toronto, CanadaFounded 202213300+ followers
Updated 4 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Drilling operations traditionally rely on multiple diesel gensets, leading to constant throttling, excess run times, and high fuel consumption. This results in operational inefficiencies, increased costs (ranging from $1M to $5M annually), and significant greenhouse gas emissions. Dynamic power demands and low-load operations also cause frequent maintenance issues and potential engine damage, leading to operational downtime.

Solution

CleanDesign offers a Hybrid Energy Management System (hEMS) that integrates battery energy storage and engine automation to optimize power loads for drilling operations. The system smooths power loads and optimizes genset usage, significantly reducing fuel consumption and emissions. By using a Load-Dependent Starting System and machine learning for real-time optimization, hEMS automatically manages genset operations, enhances efficiency, reduces maintenance costs, minimizes the risk of blackouts, and improves overall power reliability. The system gathers data at a millisecond level, enabling predictive maintenance and reduced downtime.

Target Audience

The primary customers are drilling companies and operators seeking to reduce fuel consumption, lower emissions, minimize operational downtime, and improve the efficiency of their drilling operations.

Features

  • Battery energy storage system integrated with engine automation for optimized energy usage.
  • Load-Dependent Starting System that automatically manages genset operations based on power demand.
  • Machine learning algorithms for real-time optimization of energy loads and genset performance.
  • Predictive maintenance capabilities through millisecond-level data collection and analysis.
  • Advanced software and intelligent controls that dynamically adjust power loads in real-time.
  • Real-time monitoring and management of energy loads for optimized performance and cost reduction.
  • Blackout reduction through efficient power distribution management.
This profile is AI-generated and may contain inaccuracies.