Optimize Energy Consumption and Yield in Energy-Intensive Industries With Statistical Methods

Are you looking to reduce costs, increase production efficiency, and minimize the impact of your industry on the environment? 

All industries, especially energy-intensive ones like iron and steel, injection molding, cement and petrochemicals, are under constant pressure to meet these challenges. Supply chain pressures, sustainability, stringent regulatory landscapes, increasing consumer expectations and the global energy crisis, which has caused an explosion in energy prices, have put their margins and profitability under pressure. 

According to Plastics Technology, significant energy savings are available to most injection molding operations. In a typical injection molding facility, energy consumption is often distributed as follows:

• 60-80% for heating and cooling the mold and plastic.
• 10-30% for running motors and drives.
• 5-10% for auxiliary equipment like conveyors and dryers

Time and money spent on reducing power requirements will lower those large utility bills every month. How much could be saved if a shop reduced energy consumption by just 5% or 10%? Thousands of dollars.

With high stakes, industries need systematic and efficient approaches that can quickly identify optimal process settings, save time, cost and resources.  

The most common method for fixing quality issues and developing process improvements is the one-factor-at-a-time approach. However, this method can be time-consuming and expensive and often results in suboptimal outcomes. In addition, it might be difficult to obtain any information about how the different factors might impact each other. 

One alternative is to change all parameters settings together, simultaneously, thanks to a Design of Experiments (DOE) approach.

WHAT IS Design of experiments (doe)?

There is a powerful and structured tool that can help overcome these challenges: Design of Experiments (DOE). DOE is a statistical approach that enables users to investigate the influence of several variables on the outcome(s) of interest, making it an effective tool for optimizing processes and products. It involves conducting a series of experiments in which input factors are systematically varied and the resulting output responses are measured. 

Benefits of using doe

There are many benefits of using DOE, including: 

• It enables you to identify the most critical process variables that impact energy consumption and yield. By optimizing these variables, you can reduce energy consumption and waste while improving the overall efficiency of the manufacturing process. This can help you maintain or even improve productivity and quality while reducing your environmental impact. 
• Another advantage of using DOE is that it can help you save time and resources compared to traditional trial-and-error methods. By running a set of experiments that systematically vary the input factors, you can quickly identify the optimal process settings, saving you both time and money. This is because you can reduce the amount of testing needed, which in turn can help you reduce experimental costs. 
• DOE allows for a deeper understanding of the manufacturing process and its underlying mechanisms. By providing a systematic approach to exploring the impact of multiple process variables on the outcome of interest, you can gain insights that may not be possible with other methods. This can lead to further improvements and innovations, as you have a better understanding of how your process works. 

doe with minitab

Fortunately, Minitab’s powerful software can assist with your DOE: 

• Minitab provides a variety of DOE techniques, like Screening, Factorial, and Response Surface Designs to help you create your experiments and determine the appropriate number of runs and replicates for the experiment to achieve the desired level of precision.
• Minitab can also analyze the results of experiments and identify the key process factors that have the greatest impact on energy consumption, yield and other responses.
• You can determine the optimal settings for the key process factors using optimization techniques such as Desirability Analysis and Response Optimization.
• Minitab can provide real-time data analysis and alerts to identify and correct any deviations from the optimal process settings, making it easier to run verification experiments which confirm whether the optimal solution provided by the DOE is valid or not.
• Finally, Minitab can help companies document the results of experiments and share them with other stakeholders effectively.

Want to Learn More? Visit the Education Hub.

Minitab Statistical Software can help companies reduce energy consumption and improve yield by providing an efficient approach to process optimization through DOE. With its advanced analytical capabilities and data democratization, Minitab enables companies to leverage the most of their workforce and to achieve their desired outcomes with minimal energy consumption and waste. 

Want to See a Food and Beverage Example? Check Out Our White Paper