Today a lot of companies that produces processing machines is competing to make their product more efficient than their competitors in order to improve sales. This paper is focused on processing machines that measure weight and uses mechanical force to rotate goods to increase surface contact with a processing agent. This study will investigate combinations of algorithms that is most suitable for extracting a gradient from a noisy load signal containing a periodic disturbance. A better extraction of the gradient, which is the flowrate of the processing agent, would allow the processing machine to make better predictions for its process control. 

This study applies quantitative research methods through simulations and one experiment in order to get an understanding of each algorithm’s behavior. In each test they are tested according to certain criteria, these are phase-lag, similarity, accuracy and precision of both load and gradient. 

The results of this study suggest three different algorithms that are likely to perform well in the mentioned criteria. Kalman CV is the best choice for gradient extraction on one second intervals when tracking a periodic signal is not necessary. Kalman CV-Kalman PLI is the best choice for both load and gradient extraction when tracking a periodic signal is needed for process control. It is also the best choice for extracting a gradient on sample-to-sample intervals overall. Finally, Chebyshev is the best choice for load extraction if tracking a periodic signal is not desired. 

Since this paper could only determine which algorithm is more likely to perform better, it can only partially answer its research questions. Despite only a partial answer, this paper concludes that its findings could still be used by companies to find suitable algorithms for their processing machines or be used by those new to the field to get an introduction to the presented algorithms.