Resources like water, energy, raw materials are critical for the production. HutGrip helped a carpet manufacturer from Georgia compare and analyze how changing different parameters in the production environment affects the consumption. Having this information, they were able to take practical steps on maximizing the efficiency of the process and reducing the used resources.
We monitored multiple parameters of a Skein Dye Beck (dyeing process), taking the data straight from the company’s existing control system without introducing any new sensors. There was almost no installation cost and they were able to see their data in the system right away.
A high-quality Wool carpet manufacturer in US
The yarn dying process is very much dependable on the external environment. Dyeing is done in a special solution containing dyes and particular chemical material. After dyeing, dye molecules have uncut chemical bond with fiber molecules. The temperature and time controlling are two key factors in dyeing. We started working with the carpet manufacturer to help them minimize the time and resources spent in the dying process. The carpet company wanted to reduce the additional cycles in their yarn dyeing process. In the Summer they had to dye a product up to 9 times before they achieve the desired shade. They knew out of experience that temperature, humidity levels and others affect the process and the shade of the end product. We connected to the PID controller that was already in place in their dyeing beck and started gathering operational data. Together with the temperature data, we decided to track the energy consumption data too.
Measure & Collect Data
The measured parameters are:
- Process Temperature – measured temperature
- Setpoint – desired temperature – the temperature that it is supposed to be controlled to
- Output – the power input to the process in 0-100% (the energy needed to reach the Setpoint).
The dying started at around 5:50am in the morning. The chemical formula for achieving the specified shade of the yarn was placed in the container. The process temperature gradually raised from 78F to 220F and in the next one hour. Then at 7am the dyer took out a sample of the end product and observed that the shade isn’t correct. They had to go back to the lab and prepare another chemical solution to adjust the shade. This is called one additional cycle and as you can see from the data it took about 1 hour in the lab, preparing the new solution and then about 20mins in the container. The goal is to minimize the additional cycle as this will result in minimizing:
- the time needed for going from the raw product(the yarn) to the finished end product (in this case the colored yarn)
- reduce water consumption
- reduce the energy used in the process
You see here that they needed to do 8 additional cycles to achieve the desired color and it took them more than half their work day to make all these little adjustments to the shade.
The plant manager looked at the HutGrip report above and saw that the energy data feed should be much more proportional. These fluctuations weren’t normal and this mean that by tweaking the parameters of their controllers and inspecting the pumps and valves involved in the process, they can make it much more energy efficient.
Energy consumption is one of their biggest costs and therefore we decided to focus all our efforts there. The quest now was, how can we increase the energy efficiency.
ze of the experiments were set in the design phase before the testing begun.
HutGrip allowed the plant manager to do multiple experiments with different setting for the controllers in charge for the dying process and then analyze the results. In Six Sigma this process is called Design of Experiments(DoE). The DoE approach plans for all possible dependencies in the first place, and prescribes exactly what data sets are required for assessment i.e. whether input variables change the response on their own, when combined, or not at all. In our case the variables we had were – Process Temperature, Setpoint and Output. The exact length and size of the experiments were set in the design phase before the testing begun.
HutGrip gave the company a more systematic approach to the problem. All data was automatically captured and stored in the system. The HutGrip analysis tools helped the team to compare the experiments results and identify the best control parameters to achieve proportional energy usage. At the end this helped the company increase the energy efficiency and replicate the results to all their other dying containers.