Services

First of all, we conduct a free evaluation to check if your process can benefit from modern machine learning methods. It might be the case that your process or your data is not quite ready so we might recommend installing additional sensors or changing the sampling frequency etc.

If we established that your process and data are ready then the next step is to build a simplified model of your process that will combine process and financial data and will allow calculating the unit cost. Complex relationships between process elements will be replaced by simplified equations. This model can be used as by process engineers and operators as well as by economists to analyze and improve the efficiency of the process.

Step 2: using the simplified process model, which gives us the unit cost, we will analyze your historical process data. This allows us to understand basic process characteristics (e.g. variation) and identify the periods when the process showed maximum efficiency along with the time when the process performance was suboptimal. These findings will then be used to develop initial recommendations for optimizing the process

Step 4: equipment and processes based on complex physical and chemical laws are difficult to model in conventional ways. Neural Networks will be used to create the identical digital twin of the process with all fully the process properties and behaviours fully replicated. This digital twin will be used to find more accurate and complete settings for maximum process efficiency. It can also be used to conduct offline 'what if' experiments.

Step 3: we will then connect the simplified model to the real time process data from sensors. This will allow you to track the cost of production in real time. Additionally, you will get the process performance indicators expressed in the unit cost terms and weekly/monthly KPI reports with the full analysis of the process performance.

Step 5: The final step is then the digital twin is connected to the live data from the sensors to provide recommendations for running the process at the maximum level of efficiency in real time. Additionally, It will also detect anomalies in the process or equipment behaviour. Most importantly, the model will continue to learn from new incoming data to ensure it stays uptodate and relevant expert system.