Revolutionizing Manufacturing: An Easy transition to Energy Efficiency
In a world where manufacturing industries are constantly seeking ways to reduce operational costs and mitigate environmental impact, BeChained emerges as a game-changer.
Globally, our estimates show that AI has the potential to deliver additional total economic activity of approximately $13 trillion by 2030.2 And approximately $1 trillion in value remains to be captured from the industrial sector…
Although AI adoption remains low in the industrial sector, value can be extracted today from existing infrastructure. According to our research, operators that have applied AI in industrial processing plants have reported a 10 to 15 percent increase in production and a 4 to 5 percent increase in EBITA. (McKinsey)
BeChained’s innovative AI solution has promised to make production processes 19% more energy efficient, tapping into a significant portion of the $700 billion (Total Addressable Market, since 2020) for energy optimization in manufacturing.
BeChained’s approach starts from paving the path way for Industry 5.0:
- Enhancing digital maturity in manufacturing plants,
- Reducing energy consumption on continuous basis for industry with heavy assets;
- Lowering carbon emissions and making customers accountable, therefore contributing to a more sustainable future (SDG 11 and SDG 13);
- Offering demand-response assets to smooth unexpected peaks in electric grids, while adopting more renewable sources (SDG 7 and SDG 9).
AI solution has promised to make production processes 19% more energy efficient
This customer journey starts by acknowledging what happens in the factories. If the customer has (and should have) historical data from consumption and production scheduling, great starting point!
Otherwise, BeChained will collect data and learn consumption and production pattern from real time measurements.
BeChained makes it easy for the customers: a very light investment, to equip machines with smart meters.
BeChained only requires to install a software gateway (i.e. a Linux virtual machine), integrated into the existing customers facilities, to gather data from the internal sources.
Then, it sends them out to its cloud, through the client’s VPN (Virtual Private Network) to vehicle information back and forth, while keeping security high against tampering or cyber attacks.
Let’s follow the data flow to understand how BeChained works.
Step 1: Gathering Energy Consumption Data
BeChained starts its process by gathering energy consumption data through smart meters connected to various manufacturing machines. These meters are not just passive recorders but active participants in the data collection process, leveraging modbus technology. This first step is crucial in identifying the baseline of energy usage and spots where inefficiencies are most pronounced.
Step 2: Collecting Additional Data
In tandem with energy data, BeChained collects information about machine settings from the Manufacturing Execution System (or MES), via the SCADA protocol, and production schedules from the ERP system. This comprehensive data collection approach ensures that every aspect of the manufacturing process is considered for optimization.
to vehicle information back and forth, while keeping security high against tampering or cyber attacks
Step 3: Sending Data to the Cloud
Once collected, the data is grouped with an MQTT client (within the gateway) and securely sent to the AWS cloud via a VPN.
This step is represented by the merging of data streams, traveling through a secure tunnel, and arriving at a cloud platform where the real magic begins.
As BeChained exploits different standard industrial protocols to gather, group and send data from factories to cloud system, no proprietary hardware is needed.
Step 4: Data Processing with AI
AI systems can be used to discover relevant rules through supervised and unsupervised learning from large amounts of process data. Instead of subject matter experts identifying all the rules and relationships governing a process, AI can detect patterns and insights that are not easily visible to humans. (McKinsey)
Inside the cloud, the data is stored in a time stream database and processed by advanced reinforcement learning models.
This phase is where BeChained’s AI capabilities shine, analyzing the data through complex algorithms and neural networks to unearth optimal energy usage patterns.
Step 5: Creating and Executing Instructions
The AI’s analysis results in a combination of optimal settings for machines, i.e. pressure and flows in water pumps or blade speed in paper pulpers. These settings are then converted into SCADA instructions, which are sent back to the Manufacturing Execution System, ready to be executed.
Step 6: Automated Execution in Production
The final step sees these SCADA instructions being automatically executed on the production floor. BeChained fine tunes machines’ setting and therefore their operations in real-time, to optimize energy use.
This not only reduces energy consumption, but also enhances the overall efficiency of the manufacturing process.
The customers benefit is double: from continuously reduced energy costs to equally lower spend from carbon compensation of the manufacturing emissions.
In other terms, BeChained delivers carbon offset costs reduction through carbon insetting. Look at the following table to learn the differences between the insetting and offsetting.
BeChained stands at the forefront of industrial innovation, offering a potent combination of AI, IoT, and cloud computing to revolutionize energy efficiency in manufacturing.
BeChained mainly addresses industrial manufacturers, as accountable for intensive energy needs.
The key markets are in the USA, UK, France, Germany, Italy, and Nordics. They have demonstrated to be accountable of the 70% of the energy efficiency initiatives, since 2020. For BeChained, this is the Service Addressable Markets.
By targeting those markets, AI algorithms like BeChained’s is the best cost effective solution to deliver efficient production.
It paves the way not only for more competitiveness, through cost reduction, but also it is a step towards a more responsible consumption (SDG 11) and a sustainable impact (SDG 13) for the future of industrial manufacturers.
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