We offer end to end Engineering Simulation Services to Design, Debottleneck , Enhance Equipment and Process Performance to achieve
| Improved Product Quality | Lower Energy Consumption |
| Improved Mixing | Uniform Solid Suspension |
| Uniform Gas dispersion | Reduce Power Consumption |
| Reduce Pollutants | Improved Heat transfer |
| Efficient Combustion | Pressure Drop Reduction |
The first step towards debottlenecking is to understand to the customer’s expectations, issues, pain areas very clearly. We believe that the equipment performance can be enhanced by thorough understanding of the process and the operation of the equipment. The process can be extremely complex like polymerization, gas-liquid-solid slurry reactor operating at high temperature and high pressure. Understanding of the process and customers pain can help to lead to the root cause of the issue and hence model the physics of the cause. For example, wide distribution of molecular weight of the polymer chains could be due to inefficient mixing. The inefficiency of the liquid-solid catalytic reaction could be due to improper solid suspension.
Solution with AI Methodology
In the situation when the physics is very complex and the kinetics, chemical reactions, properties etc are not available in great details, and involves a lot of inputs /streams /variables, we recommend the approach of Artificial Intelligence. This approach needs a historical data over a long period covering variability in the process and parameters involved. More critical is the data of output variables like quality, yield, conversions, composition etc.
The historical data is first cleaned and smoothened to eliminate noise and wrong data. The critical aspect o this approach is data analytics where the available data is analyzed thoroughly to understand the process variations and any underlying trends in different input variables (Xs) and output variables (Ys).
Once the data is cleaned, we use the combination of AI tools and our understanding, expertise to filter out few variables for further analysis from a large number of X’s.
We use supervised Regression method, optimize hyper parameters based on mean square errors, MAPE and R2.
A model with satisfies the statistical error criteria is used for analysis. Trend Analysis, Feature Importance gives an in-depth under standing of important variables affecting the output, the sensitivity of X’s on Y.
The model is now ready for online deployment!
Final Delivery
A detailed report in the form of presentation or/and document is provided with all the results, analysis and the engineering drawings, optimal operating conditions for the implementation is submitted.
The debottlenecking methodology involves the application of various tools and techniques based on the nature of the problem statement.
Primarily, to start with a process related problem, we adopt chemical engineering based process modeling and/or CFD modeling techniques.
If the problem is very complex with less insight into the physics and involves many variables, we follow AI based methodologies.
To address the problems that involves structural, thermal and flow coupling, we use FEA and CFD techniques.