The impact of clean-in-place parameters on rinse water effectiveness and efficiency

Experimental investigation of laminar and turbulent displacement of residual oil film

Residual oil films on pipe walls are a common occurrence in industrial processes, and their presence can significantly impact system efficiency and performance. However, the mechanisms that govern oil film removal by an immiscible displacing fluid from the internal walls of pipes under different flow regimes, including laminar and turbulent flows, are not yet fully understood. In this study, we investigated the impact of displacing fluid flow regime, injected volume, displacement time, and wall shear stress on the efficiency of residual oil film removal in a pipe. We first verified the applicability of our developed oil film measurement method for the use in vertical pipes, and found that gravity did not significantly affect the long-term oil film removal process. We verified that our results from the laminar cases agree with the theoretical thin-film limit scaling under reasonable assumptions of constant shear stress and negligible surface tension. We then examined the displacement efficiency of residual oil film under laminar and turbulent flow regimes. Our experimental results revealed that the onset of turbulence of displacing fluid played an important role in the efficient removal of residual oil film, with an optimal range of Reynolds numbers (7000-8000) when the injected volume of displacing fluid is limited. Furthermore, we explored the combined effect of wall shear stress and displacement time on the displacement process under different turbulent flow regimes. We found that the intermediate turbulent regime was the most efficient for achieving cleaning in a limited time, while the highly turbulent regime proved to be the most effective for achieving complete cleaning over a longer time period. These findings have important implications for oil recovery and pipeline maintenance and provide valuable insights into optimizing the removal of residual oil film in pipes.

Molecular Understanding of Fouling Induction and Removal: Effect of the Interface Temperature on Milk Deposits

Molecular details concerning the induction phase of milk fouling on stainless steel at an elevated temperature range were established to better understand the effect of temperature on surface fouling during pasteurization. The liquid-solid interface that replicates an industrial heat exchanger (≤75°C), including four stages (preheating, heating, holding, and cooling), was investigated using both a quartz crystal microbalance (QCM-D) and a customized flow cell. We found that the milk fouling induction process is rate-limited by the synergistic effects of bulk reactions, mass transfer, and surface reactions, all of which are controlled by both liquid and surface temperatures. Surface milk foulant becomes more rigid and compact as it builds up. The presence of protein aggregates in the bulk fluid leads to a fast formation of surface deposit with a reduced Young's modulus. Foulant adhesion and cohesion strength was enhanced as both interfacial temperature and processing time increased, while removal force increased with an increasing deposit thickness. During cleaning, caustic swelling and removal showed semilinear correlations with surface temperature (T_S), where higher T_S reduced swelling and enhanced removal. Our findings evidence that adsorption kinetics, characteristics of the foulant, and the subsequent removal mechanism are greatly dependent on the temperature profile, of which the surface temperature is the most critical one.

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes.