The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform

Virendra M Puri - One of the best experts on this subject based on the ideXlab platform.

  • Conventional and Emerging Clean-in-Place Methods for the Milking Systems
    Raw Milk, 2018
    Co-Authors: Xinmiao Wang, Ali Demirci, Robert E. Graves, Virendra M Puri
    Abstract:

    Abstract The health benefits of consuming dairy products have been demonstrated by many studies in recent years. Additionally, dairy production and consumption keep increasing worldwide. Therefore, it is of great importance to guarantee the quality and safety of raw milk, which is the origin of all consumed dairy products including liquid and powdered milk, cream, ice cream, cheese, yoghurt, and whey protein. It is even more important to ensure the milk quality if raw milk is consumed. On a typical dairy farm, milk is transported through a set of pipelines from the milking mammal to a storage tank, where it is cooled. The pipeline assembly along with the affiliated equipment is referred to as the milking system. The cleanliness of the milking system directly affects the milk quality and, therefore, a thoroughly cleaned and sanitized milking system is needed to safeguard against the potential contamination of raw milk. Also, having a low microbial load in the produced raw milk can provide extra incentive for the farmers such as cash bonuses and other special recognitions. Therefore, proper Clean-in-Place (CIP) process is indispensable to assure cleanliness. During the past decades, the cleaning and sanitizing approaches of the milking systems have been studied and improved for better technical performance and energy reduction. This chapter summarizes several conventional milking system chemical cleaning and sanitizing methods using chemical solutions and novel electrolyzed water solutions and the emerging one-step CIP method along with the CIP approaches for the automatic milking robots.

  • Optimization and modeling of an electrolyzed oxidizing water based Clean-in-Place technique for farm milking systems using a pilot-scale milking system
    Journal of Food Engineering, 2014
    Co-Authors: S. R. S. Dev, Ali Demirci, Robert E. Graves, Virendra M Puri
    Abstract:

    Abstract Electrolyzed oxidizing (EO) water has been recommended to be used as a cleaning and sanitizing agent for Clean-in-Place (CIP) of on-farm milking systems. The CIP process for milking system with EO water was optimized using a pilot-scale pipeline milking system. The milking system was soiled using raw milk inoculated with four common microorganisms found in milk and cleaned using EO water. The cleaning effectiveness of the EO water treatment was evaluated by ATP bioluminescence test and microbiological analysis through enrichment culture. The effect of different temperatures of the alkaline EO water (45–75 °C) and acidic EO water (25–45 °C) were investigated. A generalized mathematical model was built and validated for the pilot scale milking system as well as compared with the conventional CIP technique. This study indicated that the EO water CIP outperformed the conventional CIP, indicating that it has a potential to be used in commercial dairy farms.

  • MATHEMATICAL MODELING AND OPTIMIZATION OF Clean-in-Place BY USING ELECTROLYZED OXIDIZING WATER FOR A PILOT-SCALE MILKING SYSTEM
    2013 Kansas City Missouri July 21 - July 24 2013, 2013
    Co-Authors: S. R. S. Dev, Ali Demirci, Robert E. Graves, Virendra M Puri
    Abstract:

    Abstract. Electrolyzed oxidizing (EO) water, produced by the electrolysis of separating a weak sodium chloride solution into alkaline and acidic components, has the potential to be used as a cleaning and sanitizing agent for Clean-in-Place (CIP) cleaning of on-farm milking systems. To demonstrate this, a pilot-scale pipeline milking system was constructed and soiled using raw milk inoculated with four common microorganisms found in milk. The milking system was then washed with alkaline EO water followed by acidic EO water according to the experimental design by using surface response methodology. After cleaning, the effectiveness of the EO water treatment was evaluated by ATP bioluminescence and microbiological analysis through enrichment culture. The effect of different temperatures of the alkaline and acidic EO water were investigated and optimized. By determining the logarithmic mean temperature for the treatment for both the alkaline and acidic EO water and by comparing values obtained from conventional heat transfer calculation versus the actual experimental data, a generalized mathematical model was built as a set of algebraic equations in order to determine the effective treatment temperatures for different configurations of the milking equipment based on the maximum reduction in ATP bioluminescence values. The model thus obtained was validated for the pilot scale milking system through validation and comparison studies

Murray Moo-young - One of the best experts on this subject based on the ideXlab platform.

  • Clean-in-Place systems for industrial bioreactors: Design, validation and operation
    Journal of Industrial Microbiology, 1994
    Co-Authors: Yusuf Chisti, Murray Moo-young
    Abstract:

    Guidelines for design, validation and operation of Clean-in-Place systems for industrial fermentation plant are presented. Design of vessels, surface finishes, materials of construction, types and locations of valves are some of the considerations addressed. Requisite levels of turbulence for cleaning of pipes and vessels are discussed as well as typical cleaning sequences. Recommendations for validation of cleaning are presented and the significance of design of cleaning systems in ensuring satisfactory validation is pointed out. To the extent possible, validation of cleaning should be carried out with real process soil or soil closely simulating actual fermentation broths.

  • Short Review Clean-in-Place systems for industrial bioreactors: design, validation and operation
    1994
    Co-Authors: Yusuf Chisti, Murray Moo-young
    Abstract:

    SUMMARY Guidelines for design, validation and operation of Clean-in-Place systems for industrial fermentation plant are presented. Design of vessels, surface finishes, materials of construction, types and locations of valves are some of the considerations addressed. Requisite levels of turbulence for cleaning of pipes and vessels are discussed as well as typical cleaning sequences. Recommendations for validation of cleaning are presented and the significance of design of cleaning systems in ensuring satisfactory validation is pointed out. To the extent possible, validation of cleaning shOuld be carried out with real process soil or soil closely simulating actual fermentation broths.

Alessandro Simeone - One of the best experts on this subject based on the ideXlab platform.

  • Ultrasonic measurements and machine learning for monitoring the removal of surface fouling during Clean-in-Place processes
    2020
    Co-Authors: Josep Escrig-escrig, Elliot Woolley, Alessandro Simeone, S. Rangappa, A Rady, Nicholas Watson
    Abstract:

    Cleaning is an essential operation in the food and drink manufacturing sector, although it comes with significant economic and environmental costs. Cleaning is generally performed using autonomous Clean-in-Place (CIP) processes, which often over-clean, as suitable technologies do not exist to determine when fouling has been removed from the internal surfaces of processing equipment. This research combines ultrasonic measurements and machine learning methods to determine when fouling has been removed from a test section of pipework for a range of different food materials. The results show that the proposed methodology is successful in predicting when fouling is present on the test section with accuracies up to 99% for the range of different machine learning algorithms studied. Various aspects relating to the training data set and input data selection were studied to determine their effect on the performance of the different machine learning methods studied. It was found that the classification models performed better when data points were extracted directly from the ultrasonic waves and when data sets were combined for different fouling materials

  • Clean-in-Place monitoring of different food fouling materials using ultrasonic measurements
    Food Control, 2019
    Co-Authors: Josep Escrig, Elliot Woolley, Alessandro Simeone, S. Rangappa, N J Watson
    Abstract:

    Abstract Clean-in-Place is an autonomous technique used to clean the internal surfaces of processing equipment in the food and drink sector. However, these systems clean for a longer time than required with negative economic and environmental impacts. In this work, an ultrasonic sensor system was developed to monitor the cleaning of different food fouling materials at laboratory scale. The fouling removal of three different food materials was also studied at different cleaning fluid temperatures. The three food materials had different cleaning mechanisms, which could be monitored successfully with the ultrasonic system. Tomato paste and gravy appeared to be cleaned by mechanical forces whereas malt extract dissolved into the cleaning water. The results yielded from the cleaning of the malt was found to be repeatable whereas the tomato and gravy were more variable between repeat experiments. It was found that changes in recorded ultrasonic signals were mainly affected by the area of fouling that covered the transducer's active element.

  • Enhanced Clean-in-Place Monitoring Using Ultraviolet Induced Fluorescence and Neural Networks.
    Sensors (Basel Switzerland), 2018
    Co-Authors: Alessandro Simeone, N J Watson, Bin Deng, Elliot Woolley
    Abstract:

    Clean-in-Place (CIP) processes are extensively used to clean industrial equipment without the need for disassembly. In food manufacturing, cleaning can account for up to 70% of water use and is also a heavy user of energy and chemicals. Due to a current lack of real-time in-process monitoring, the non-optimal control of the cleaning process parameters and durations result in excessive resource consumption and periods of non-productivity. In this paper, an optical monitoring system is designed and realized to assess the amount of fouling material remaining in process tanks, and to predict the required cleaning time. An experimental campaign of CIP tests was carried out utilizing white chocolate as fouling medium. During the experiments, an image acquisition system endowed with a digital camera and ultraviolet light source was employed to collect digital images from the process tank. Diverse image segmentation techniques were considered to develop an image processing procedure with the aim of assessing the area of surface fouling and the fouling volume throughout the cleaning process. An intelligent decision-making support system utilizing nonlinear autoregressive models with exogenous inputs (NARX) Neural Network was configured, trained and tested to predict the cleaning time based on the image processing results. Results are discussed in terms of prediction accuracy and a comparative study on computation time against different image resolutions is reported. The potential benefits of the system for resource and time efficiency in food manufacturing are highlighted.

  • Enhancement of Clean-in-Place procedures in powder production using ultraviolet-induced fluorescence
    Procedia CIRP, 2018
    Co-Authors: Elliot Woolley, Abe Wanjeri, Alessandro Simeone
    Abstract:

    Abstract In powder manufacturing facilities, Clean-in-Place (CIP) is a technique utilised for cleaning internal surfaces of equipment without having to dismantle them. However, the process is commonly based an open loop approach with parameter settings dependent on estimation and assumptions, leading to increased energy consumption and decreased process availability. It has previously been demonstrated that ultraviolet-induced fluorescence of proteins can be used for the detection of liquid foodstuffs in order to reduce CIP cycle times. This research investigates the suitability of an RGB vision sensor for detecting ultraviolet induced fluorescence of proteins in a range of powders (foodstuffs and detergents) under a simulated cleaning process. An image processing procedure is described that enables the evaluation of remaining fouling within a sample. The sensitivity of the system is demonstrated by comparing the coverage (area) of fouling against the volume remaining (weight). The suitability of the technique for industrial application is discussed.

  • Eco-intelligent Monitoring for Fouling Detection in Clean-in-Place
    Procedia CIRP, 2017
    Co-Authors: Alessandro Simeone, Alfredo Zendejas Rodriguez, Elliot Woolley, Shahin Rahimifard
    Abstract:

    Clean-in-Place (CIP) is a widely used technique applied to clean industrial equipment without disassembly. Cleaning protocols are currently defined arbitrarily from offline measurements. This can lead to excessive resource (water and chemicals) consumption and downtime, further increasing environmental impacts. An optical monitoring system has been developed to assist eco-intelligent CIP process control and improve resource efficiency. The system includes a UV optical fouling monitor designed for real-time image acquisition and processing. The output of the monitoring is such that it can support further intelligent decision support tools for automatic cleaning assessment during CIP phases. This system reduces energy and water consumption, whilst minimising non-productive time: the largest economic cost for CIP.

Elliot Woolley - One of the best experts on this subject based on the ideXlab platform.

  • Ultrasonic measurements and machine learning for monitoring the removal of surface fouling during Clean-in-Place processes
    2020
    Co-Authors: Josep Escrig-escrig, Elliot Woolley, Alessandro Simeone, S. Rangappa, A Rady, Nicholas Watson
    Abstract:

    Cleaning is an essential operation in the food and drink manufacturing sector, although it comes with significant economic and environmental costs. Cleaning is generally performed using autonomous Clean-in-Place (CIP) processes, which often over-clean, as suitable technologies do not exist to determine when fouling has been removed from the internal surfaces of processing equipment. This research combines ultrasonic measurements and machine learning methods to determine when fouling has been removed from a test section of pipework for a range of different food materials. The results show that the proposed methodology is successful in predicting when fouling is present on the test section with accuracies up to 99% for the range of different machine learning algorithms studied. Various aspects relating to the training data set and input data selection were studied to determine their effect on the performance of the different machine learning methods studied. It was found that the classification models performed better when data points were extracted directly from the ultrasonic waves and when data sets were combined for different fouling materials

  • Clean-in-Place monitoring of different food fouling materials using ultrasonic measurements
    Food Control, 2019
    Co-Authors: Josep Escrig, Elliot Woolley, Alessandro Simeone, S. Rangappa, N J Watson
    Abstract:

    Abstract Clean-in-Place is an autonomous technique used to clean the internal surfaces of processing equipment in the food and drink sector. However, these systems clean for a longer time than required with negative economic and environmental impacts. In this work, an ultrasonic sensor system was developed to monitor the cleaning of different food fouling materials at laboratory scale. The fouling removal of three different food materials was also studied at different cleaning fluid temperatures. The three food materials had different cleaning mechanisms, which could be monitored successfully with the ultrasonic system. Tomato paste and gravy appeared to be cleaned by mechanical forces whereas malt extract dissolved into the cleaning water. The results yielded from the cleaning of the malt was found to be repeatable whereas the tomato and gravy were more variable between repeat experiments. It was found that changes in recorded ultrasonic signals were mainly affected by the area of fouling that covered the transducer's active element.

  • Enhanced Clean-in-Place Monitoring Using Ultraviolet Induced Fluorescence and Neural Networks.
    Sensors (Basel Switzerland), 2018
    Co-Authors: Alessandro Simeone, N J Watson, Bin Deng, Elliot Woolley
    Abstract:

    Clean-in-Place (CIP) processes are extensively used to clean industrial equipment without the need for disassembly. In food manufacturing, cleaning can account for up to 70% of water use and is also a heavy user of energy and chemicals. Due to a current lack of real-time in-process monitoring, the non-optimal control of the cleaning process parameters and durations result in excessive resource consumption and periods of non-productivity. In this paper, an optical monitoring system is designed and realized to assess the amount of fouling material remaining in process tanks, and to predict the required cleaning time. An experimental campaign of CIP tests was carried out utilizing white chocolate as fouling medium. During the experiments, an image acquisition system endowed with a digital camera and ultraviolet light source was employed to collect digital images from the process tank. Diverse image segmentation techniques were considered to develop an image processing procedure with the aim of assessing the area of surface fouling and the fouling volume throughout the cleaning process. An intelligent decision-making support system utilizing nonlinear autoregressive models with exogenous inputs (NARX) Neural Network was configured, trained and tested to predict the cleaning time based on the image processing results. Results are discussed in terms of prediction accuracy and a comparative study on computation time against different image resolutions is reported. The potential benefits of the system for resource and time efficiency in food manufacturing are highlighted.

  • Enhancement of Clean-in-Place procedures in powder production using ultraviolet-induced fluorescence
    Procedia CIRP, 2018
    Co-Authors: Elliot Woolley, Abe Wanjeri, Alessandro Simeone
    Abstract:

    Abstract In powder manufacturing facilities, Clean-in-Place (CIP) is a technique utilised for cleaning internal surfaces of equipment without having to dismantle them. However, the process is commonly based an open loop approach with parameter settings dependent on estimation and assumptions, leading to increased energy consumption and decreased process availability. It has previously been demonstrated that ultraviolet-induced fluorescence of proteins can be used for the detection of liquid foodstuffs in order to reduce CIP cycle times. This research investigates the suitability of an RGB vision sensor for detecting ultraviolet induced fluorescence of proteins in a range of powders (foodstuffs and detergents) under a simulated cleaning process. An image processing procedure is described that enables the evaluation of remaining fouling within a sample. The sensitivity of the system is demonstrated by comparing the coverage (area) of fouling against the volume remaining (weight). The suitability of the technique for industrial application is discussed.

  • Eco-intelligent Monitoring for Fouling Detection in Clean-in-Place
    Procedia CIRP, 2017
    Co-Authors: Alessandro Simeone, Alfredo Zendejas Rodriguez, Elliot Woolley, Shahin Rahimifard
    Abstract:

    Clean-in-Place (CIP) is a widely used technique applied to clean industrial equipment without disassembly. Cleaning protocols are currently defined arbitrarily from offline measurements. This can lead to excessive resource (water and chemicals) consumption and downtime, further increasing environmental impacts. An optical monitoring system has been developed to assist eco-intelligent CIP process control and improve resource efficiency. The system includes a UV optical fouling monitor designed for real-time image acquisition and processing. The output of the monitoring is such that it can support further intelligent decision support tools for automatic cleaning assessment during CIP phases. This system reduces energy and water consumption, whilst minimising non-productive time: the largest economic cost for CIP.

David M Phinney - One of the best experts on this subject based on the ideXlab platform.

  • The impact of Clean-in-Place parameters on rinse water effectiveness and efficiency
    Journal of Food Engineering, 2018
    Co-Authors: Mengyuan Fan, David M Phinney, Dennis R. Heldman
    Abstract:

    Abstract Although Cleaning-in-Place (CIP) is abundantly used throughout the food industry, it is recognized that CIP operations use significant amounts of water and energy. The overall objective of this investigation was to evaluate parameters needed to improve the effectiveness of water use during CIP pre-rinse. A pilot-scale CIP system was operated over a range of Reynolds number (Re) from 16,000 to 260,000, while evaluating the effectiveness of rinse water to remove a reconstituted skim milk residue film from stainless steel pipe surfaces. Rinse water effectiveness was quantified by comparing the protein concentration on the pipe surface after pre-rinse to the initial level. As the Re increased, the effectiveness of rinse step increased, but not in linear proportionality. The efficiency of the rinse water decreased significantly as the volume of rinse water increased. The results of this investigation provide the basis for reducing water and energy requirements during CIP operations.

  • identification of residual nano scale foulant material on stainless steel using atomic force microscopy after clean in place
    Journal of Food Engineering, 2017
    Co-Authors: David M Phinney, Kylee R Goode, P J Frye, Dennis R Heldma, Serafim Akalis
    Abstract:

    Abstract During Clean-in-Place (CIP), solutions are pumped through process equipment to remove soils. In order to validate reductions in CIP inputs, foulants need to be detectable and quantifiable on smaller scales than current industrial practices. In this study, fluorescent microscopy was used for quantifying macroscopic cleanliness of a soiled stainless steel coupon after CIP. An asymptotic model was used to describe the removal of soil as a function of the coupon exposure time and cleaning solution temperature. From these models, cleaning parameters were determined and used to generate coupons predicted to be 99.0 and 99.9% clean. This cleanliness was verified using atomic force microscopy (AFM). AFM identified foulant on the order of 5 μm 2 on a 1.0 × 10 4  μm 2 area. AFM showed cleanliness ranging from 99.41 to 99.94%. Differences between predicted and actual cleanliness suggest a change in cleaning mechanism at different scales.