Process Improvement in Stable Bleaching Powder Reactor at Aditya Birla Science and Technology Company
Technology Category
- Sensors - Gas Sensors
- Sensors - Utility Meters
Applicable Industries
- Metals
- Oil & Gas
Applicable Functions
- Logistics & Transportation
- Product Research & Development
Use Cases
- Last Mile Delivery
- Time Sensitive Networking
About The Customer
Aditya Birla Science and Technology Company Private Limited (ABSTCPL) is the corporate research and development centre for the Aditya Birla Group. Located in Taloja, just outside of Mumbai in India, ABSTCPL supports the broad diversity of the Group’s businesses through multidisciplinary teams of expert scientists and engineers who lead fundamental and applied research projects. At ABSTCPL, research and development is based on two strong capabilities. The Process Engineering and Sciences Laboratory focuses on advanced processes and designs, process control and automation, and process engineering platforms and scale-up. The Science and Technology Platforms Laboratory provides expertise in metallurgy, fiber science and textiles, materials and surface sciences, and chemistry.
The Challenge
Aditya Birla Science and Technology Company was facing a significant challenge in their Stable Bleaching Powder (SBP) manufacturing process. The process involved the chlorination of hydrated lime by aerating an SBP solids bed with chlorine gas. However, the company was losing approximately 60 kg/batch of solids, which comprised of products, reactants, and intermediate compounds. These losses were resulting in significant time and cost inefficiencies. The goal was to minimize these losses without making major modifications to the SBP plant. The challenge was to identify process parameters that could be altered to improve the efficiency of the process. However, being a closed-loop system, onsite physical measurements were difficult to carry out.
The Solution
The company decided to use ANSYS CFD to understand the existing issues in the flow patterns. The aerated solids bed was modeled as a porous zone, and impeller rotation was captured using the frozen rotor approach, with the k-ε turbulence model. Simulations helped identify gas channeling and regions where flow velocities exceeded the terminal settling velocity of the average solids particle size. The gas injection nozzle diameters and the number of injection locations were identified as the design parameters. Simulations revealed that increasing the nozzle diameter 2.5 times reduced the outlet-velocity-to-terminal-velocity ratio to 0.57 with 92 percent more uniform flow distribution. Adding two injection locations further reduced the velocity ratio to 0.47, but some flow uniformity was lost.
Operational Impact
Quantitative Benefit
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