NTWIST provides machine learning and artificial intelligence tools that optimize industrial processes in continuous process plants, enhancing productivity while minimizing costs and resource consumption. Their solutions enable companies to track material flow and improve operational efficiency, ultimately reducing waste and greenhouse gas emissions.
Funding
$65.3K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
MIFounders
Product
Problem
Industrial plants often struggle with inefficiencies in their continuous processes due to feed variability, downtime, and suboptimal resource consumption. Siloed data sources and a lack of real-time material flow tracking hinder the ability to identify discrepancies and optimize operations. This results in increased costs, waste, and environmental impact.
Solution
NTWIST provides data-powered solutions that leverage machine learning to optimize industrial processes in continuous process plants. The platform enables companies to track material flow, identify plant/production discrepancies, and improve operational efficiency. By connecting previously siloed data sources, NTWIST helps reduce the effect of feed variability, avoid downtime, and increase peak throughput. The solutions also assist in decreasing energy and reagent consumption, minimizing waste, and reducing greenhouse gas emissions.
Target Audience
NTWIST targets industrial plants in sectors such as mining, mineral processing, and oil & gas, focusing on companies seeking to optimize their continuous processes, reduce costs, and improve sustainability.
Features
- Mine-To-Mill-To-Mine Optimization: Connects block models, fleet management systems, stockpile surveys, and plant instruments to track material flow in open pit gold mines.
- Sustainability Tools: Tracks, manages, optimizes, and reports key environmental metrics for carbon accounting and emissions tracking.
- Mineral Processing Solutions: Reduces the effect of feed variability, avoids downtime, and increases peak throughput in mills, concentrators, and leach plants.
- Oil & Gas Solutions: Analyzes historical data to identify opportunities for process control improvements and reduce variability.