Energy efficiency is the world’s first fuel and the main route to net-zero (part 2/2)

BeChained
5 min readOct 25, 2023

Continuing from here

Courtesy by: stripe.com/climate

Solution

Thus, energy metering and efficiency remains a key challenge in modern industry. Monitoring of industrial processes represents a driver for development of sustainable manufacturing automation.

New energy technologies, like green H2, are not viable yet and still need to be deeply tested in current production processes. In the short term, the industry can only start making internal processes more efficient and sustainable.

The first step is sourcing 3PL meters to:

  • digitize and monitor electric parameters (i.e. power, energy, power factor), via modbus protocol
  • optimize and reduce consumption, keeping on improving performance,
  • execute improvements automatically,
  • keep carbon footprint under control.
Digitisation of a manufacturing plant

Digitisation

To control energy resources and costs, the industry needs to:

  • install IoTs per individual machine,
Dashboard with electric measures
  • connect actual data metering in the local network and then with a cloud-based to display information,
  • build a one-stop dashboard to monitor performance,
  • implement cybersecurity measures to protect sensitive data.
MQTT and communication process from facility to BeChained'’Cloud

Optimisation and cost reduction

Manufacturing and automated processes were designed to respond to production continuity goals. Many industries with a high rate of marginal cost of energy for products push for energy efficiency.

The high level of production automation and multiple processes equally require automatic decision making. So, humanly impossible to control the input-output chain.

Cloud-based infrastructure

At BeChained, the solution is integrated in the customers’ facility, gathering:

  • energy consumption,
  • machine’s settings,
  • production scheduling (batches),
  • and driving it to the cloud-base system.

Moreover, the solution crunches data from the external sources, like energy market prices, news (i.e. Bloomberg) for price forecast (with sensitivity analysis), CO2 emissions from energy mix by Grid operators from energy markets API.

The value proposition is to reduce costs from energy consumption and offsetting emissions (carbon compensation, where applicable).

The Artificial Intelligence technology:

  • is trained with information from the bottom lines,
  • crunches data,
  • identifies the optimal settings on the machines in a production line,
  • and executing the improvements automatically over the MES.

So, we reduce the energy consumption and the CO2 emission, at once, making production more efficient:

  • exploiting autonomous decision making through Reinforcement Learning multi agent models,
  • tweaking machine’s setting,
  • executing (not only suggesting) improvements.

Continuous improvement

Energy flexibility forecast solution

The architecture consists in different components:

  • API for data acquisition from customers’ facilities,
  • API to collect and manage data from external sources (OTE2),
  • demand forecast (OTE1) for production batch,
  • consumption optimisation and instruction crafting (OTE3),
  • measure and improve performance (OTE4).

OTE4 is key to continuously making efficient processes, tweaking machine’s settings on the run. For this, we implemented the state of the art Reinforcement Learning models (in R&D, they were only tested with synthetic dataset, but we trained them with actual industrial process information).

We opted in for multi-agent algorithms to represent the digital twin of a factory, where outputs from some devices are the inputs for others.

As the graph shows up, we dynamically identify the improvement areas (with OTE1 forecast models) to:

  • shave peaks,
  • compensate downhills
  • or offer these in energy demand-response markets.

The solution is gradually implemented by processes in a production plant, from:

  • demand analysis to identify baseload, auxiliary and discontinuous loads,
  • detailing auxiliary processes, i.e. water management, paper pulpers, pumps, compressors, exhaust management;
  • finally, cracking core processes, i.e. paper machines in the paper industry.

The utilities in production processes are transversal in many heavy and light industries, like:

  • steel,
  • chemical,
  • food processing,
  • paper manufacturing.
Paper manufacturing processes in a production line

The ability to adjust and manage similar processes in different industries is one of the BeChained’s key differentiating factors.

Operation benefits

The solution runs at a 10 MW plant making tissue paper. The customer’s annual energy spent is approx than $3–5mm.

It runs on 20 machines for an end-to-end machine paper production line.

Paper mill

The solution has been tested and also runs in production also in the steel industry. As it has been more extensively tested on 24 machines (i.e. water management, compressors, heaters, and more), during the last. 6 months.

Small changes in the machine’s settings resulted in 19% energy saved, with only $20,000 invested in metering equipment, and low dedication of internal resources (30 minutes per day).

The ROI is $4 saved for each $1 invested on the solution.

The estimated impact on marginal costs of energy is between 2% and 5%. Recently, the customer committed to doubling the original scope of the project (from 24 to 46 total devices), and roll BeChained’s solution to 2 more plants (Spain & USA).

The key operational benefits are:

  • digitisation of the production processes,
  • one-stop solution to monitor energy & machine settings at different facilities,
  • continuous improvement of per-product production performance,
  • carbon footprint tracking and reporting,

The key differentiating ability is to deliver a “set it and forget it” solution which is able to make automatic “supervised” decision and executes improvements on the MES.

Finally, the carbon footprint management (carbon accounting) is another key differentiation for many industries, like food or paper making.

At BeChained, we gather consumption data from the sources (smart-meter) and collect hourly CO2 emissions for the grid supply mix.

By uploading the information through a blockchain backend, we store it in the ledger and certificate the carbon transaction, without 3PL to validate it.

While the carbon market price in the EU, Canada, and in California and Oregon peaked over $100/tCO2e, the trailblazers anticipated the future industry regulations and triggered accounting and offsetting programs.

We help conglomerates keep CO2 tracked at product level, in production processing and supply chain.

By reducing energy in processes, we inherently cut CO2 emissions, therefore also the offsetting costs.

Conclusions

The energy efficiency and carbon emission insetting are the utmost important outcomes from BeChained’s solution.

BeChained continuously delivers economic and operational results for customers,

  • cutting 19% energy consumption in processes,
  • reducing energy costs and driving down the carbon compensation costs,
  • generating a Return on Investment of 25%.

Finally, we pave the way for market competitiveness with greener products.

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BeChained

AI to eliminate wasted energy in manufacturing through energy efficiency & unlocking demand-response opportunities