Fouling during food processing – progress in tackling this inconvenient truth

Wilson, D.I.

doi:10.1016/j.cofs.2018.10.002

Cited by 28 publications

(10 citation statements)

References 82 publications

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“…In addition, they all have a different composition and therefore foul and clean from a surface differently. It has been shown in [39] that different materials yield different cleaning characteristics, and it is important to assess the potential of the developed sensing techniques for a range of industrially relevant scenarios. Such considerations highlight the need for more adaptive CIP systems in order to guarantee the removal of all fouling whilst avoiding over-cleaning.…”

Section: Experimental Testsmentioning

confidence: 99%

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

Simeone

Woolley

Escrig

et al. 2020

Sensors

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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.

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Section: Experimental Testsmentioning

confidence: 99%

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

Simeone

Woolley

Escrig

et al. 2020

Sensors

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“…Cleaning, often in the form of clean-in-place (CIP) practices, is required to restore hygiene after fouling. Therefore, fouling and cleaning processes do not occur independently; rather, they exist within a continuous cycle in industrial processes, and this cycle has significant economic and environmental consequences. , This emphasizes the need for more research studies that investigate both fouling and cleaning . Better understanding of a foulant’s structure, specifically its adhesive and cohesive forces, , as well as its composition, can help tailor a CIP cycle to meet the needs of a foulant, rather than a “one-size-fits-all” CIP protocol. − Optimized and targeted cleaning methodologies have the potential to reduce water, energy, and chemical usage.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Characterization of Milk Fouling on Stainless Steel Using a High-Pressure High-Temperature Quartz Crystal Microbalance with Dissipation

et al. 2022

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Fouling at interfaces deteriorates the efficiency and hygiene of processes within numerous industrial sectors, including the oil and gas, biomedical device, and food industries. In the food industry, the fouling of a complex food matrix to a heated stainless steel surface reduces production efficiency by increasing heating resistance, pumping requirements, and the frequency of cleaning operations. In this work, quartz crystal microbalance with dissipation (QCM-D) was used to study the interface formed by the fouling of milk on a stainless steel surface at different flow rates and protein concentrations at high temperatures (135 °C). Subsequently, the QCM-D response was recorded during the cleaning of the foulant. Two phases of fouling were identified. During phase-1, the fouling rate was dependent on the flow rate, while the fouling rate during phase-2 was dependent on the flow rate and protein concentration. During cleaning, foulants deposited at the higher flow rate swelled more than those deposited at the lower flow rate. The composition of the fouling deposits consisted of both protein and mineral species. Two crystalline phases of calcium phosphate, β-tricalcium phosphate and hydroxyapatite, were identified at both flow rates. Stratification in topography was observed across the surface of the QCM-D sensor with a brittle and cracked structure for deposits formed at 0.2 mL/min and a smooth and close-packed structure for deposits formed at 0.1 mL/min. These stratifications in the composition and topography were correlated to differences in the reaction time and flow dynamics at different flow rates. This high-temperature application of QCM-D to complex food systems illuminates the initial interaction between proteins and minerals and a stainless steel surface, which might otherwise be undetectable in low-temperature applications of QCM-D or at larger bench and industrial scales. The methods and results presented here have implications for optimizing processing scenarios that limit fouling formation while also enhancing removal during cleaning.

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“…Three materials (tomato paste, gravy, and concentrated malt) were used to foul the test sections. The materials were chosen because of their different compositions, which is known to affect surface adhesion and cleaning behaviour (Wilson, 2018). For both experimental rigs, 15 g of fouling material was placed in the centre of the plate for the flat rig, 30 mm from the exit of the pipes.…”

Section: Ultrasonic Sensingmentioning

confidence: 99%

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Watson

Bowler

Rady

et al. 2021

Front. Sustain. Food Syst.

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Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector.

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Fouling during food processing – progress in tackling this inconvenient truth

Cited by 28 publications

References 82 publications

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

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

Monitoring and Characterization of Milk Fouling on Stainless Steel Using a High-Pressure High-Temperature Quartz Crystal Microbalance with Dissipation

Intelligent Sensors for Sustainable Food and Drink Manufacturing

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