PLANT DATA ANALYSIS OF PRE-RINSE IN CLEANING-IN-PLACE (CIP) PROCEDURES FOR AN ULTRA-HIGH TEMPERATURE (UHT) UNIT OPERATION
(2025) KLTM02 20251Food Technology and Nutrition (M.Sc.)
- Abstract
- CIP systems are critical for ensuring hygienic integrity in ultra-high temperature (UHT) food processing. However, conventional cleaning cycles are often rigidly timed and overdesigned, leading to unnecessary water, energy, and chemical use. This thesis presents a data-driven framework to evaluate and optimize the prerinse phase of UHT CIP cycles using pressure drop behavior as an alternative for soil removal efficiency.
Using plant data, an empirical model for clean-system system resistance (Ksys) was developed based on sterilization-phase measurements. This model, along with theoretical formulations based on the Darcy–Weisbach equation and two friction factor estimators (Swamee–Jain and Colebrook–White), was used to calculate pressure... (More) - CIP systems are critical for ensuring hygienic integrity in ultra-high temperature (UHT) food processing. However, conventional cleaning cycles are often rigidly timed and overdesigned, leading to unnecessary water, energy, and chemical use. This thesis presents a data-driven framework to evaluate and optimize the prerinse phase of UHT CIP cycles using pressure drop behavior as an alternative for soil removal efficiency.
Using plant data, an empirical model for clean-system system resistance (Ksys) was developed based on sterilization-phase measurements. This model, along with theoretical formulations based on the Darcy–Weisbach equation and two friction factor estimators (Swamee–Jain and Colebrook–White), was used to calculate pressure drop during cleaning. However, due to underprediction and inconsistency in the theoretical methods, only the proprietary model was used for RSL calculations.
RSL was computed as a dimensionless ratio of observed to clean-state pressure drop, and RSLdrop defined from the start of prerinse to the start of caustic, was used to assess cleaning effectiveness. Regression models were developed to relate RSLdrop to prerinse flow rate, temperature, and duration, using production-linked CIP cycles with ≥360 minutes of prior operation to ensure relevant fouling. These were done for 2 specific groups split based on prerinse durations:
•For short prerinse durations (≤20 min), cleaning performance was highly sensitive to flow rate and temperature. Regression fits showed moderate predictability (R² = 0.82), but inconsistent RSLdrop behaviour suggested incomplete or unstable soil removal under suboptimal rinse exposure.
•For extended prerinse durations (>20 min), models showed stronger consistency and higher accuracy (R² = 0.87), indicating that longer exposure times allow for more complete hydration, softening, and dislodgement of fouling layers. (Less) - Popular Abstract
- In UHT (Ultra-high temperature) milk processing, keeping the equipment clean is critical. After each production run, the system is cleaned using a process called Cleaning-in-Place (CIP), where water and chemicals are circulated through the pipes without taking anything apart. It’s effective but often overused. Many plants run fixed-length cleaning cycles, just to keep them safe. This may cause wastage of water, time, and energy.
This thesis explores how to clean more efficiently by using data that factories already collect, specifically pressure readings during cleaning. The key idea is that, when pipes are dirty, water flows with more resistance. When they’re clean, the pressure drops are lower. By tracking how the pressure drops change... (More) - In UHT (Ultra-high temperature) milk processing, keeping the equipment clean is critical. After each production run, the system is cleaned using a process called Cleaning-in-Place (CIP), where water and chemicals are circulated through the pipes without taking anything apart. It’s effective but often overused. Many plants run fixed-length cleaning cycles, just to keep them safe. This may cause wastage of water, time, and energy.
This thesis explores how to clean more efficiently by using data that factories already collect, specifically pressure readings during cleaning. The key idea is that, when pipes are dirty, water flows with more resistance. When they’re clean, the pressure drops are lower. By tracking how the pressure drops change during rinsing, we can figure out how much dirt is being removed and when the system is clean enough to stop.
A model of “clean” system resistance was built using data from the sterilization phase, when the equipment is known to be clean. Then, for each cleaning cycle, a measure called Relative Soil Level (RSL) was calculated to track how much fouling was present. When RSL goes down, it means cleaning is working. The drop in RSL was used to measure the total cleaning effect.
The study analyzed chosen CIP cycles to relate RSL to prerinse flow rate, temperature, and duration. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9197290
- author
- Subramanyam, Aditya LU
- supervisor
- organization
- course
- KLTM02 20251
- year
- 2025
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Food Engineering, Food Technology and Nutrition, Cleaning-In-Place (CIP), Ultra-High Temperature (UHT) Processing, Prerinse, Residual Soil Level (RSL), System Resistance Coefficient (Ksys), Pressure Drop Modeling, Food Process Engineering, Friction Factor Estimation, Empirical vs Theoretical Modeling, Water and Energy Efficiency
- language
- English
- id
- 9197290
- date added to LUP
- 2025-06-13 14:54:44
- date last changed
- 2025-06-13 14:54:44
@misc{9197290,
abstract = {{CIP systems are critical for ensuring hygienic integrity in ultra-high temperature (UHT) food processing. However, conventional cleaning cycles are often rigidly timed and overdesigned, leading to unnecessary water, energy, and chemical use. This thesis presents a data-driven framework to evaluate and optimize the prerinse phase of UHT CIP cycles using pressure drop behavior as an alternative for soil removal efficiency.
Using plant data, an empirical model for clean-system system resistance (Ksys) was developed based on sterilization-phase measurements. This model, along with theoretical formulations based on the Darcy–Weisbach equation and two friction factor estimators (Swamee–Jain and Colebrook–White), was used to calculate pressure drop during cleaning. However, due to underprediction and inconsistency in the theoretical methods, only the proprietary model was used for RSL calculations.
RSL was computed as a dimensionless ratio of observed to clean-state pressure drop, and RSLdrop defined from the start of prerinse to the start of caustic, was used to assess cleaning effectiveness. Regression models were developed to relate RSLdrop to prerinse flow rate, temperature, and duration, using production-linked CIP cycles with ≥360 minutes of prior operation to ensure relevant fouling. These were done for 2 specific groups split based on prerinse durations:
•For short prerinse durations (≤20 min), cleaning performance was highly sensitive to flow rate and temperature. Regression fits showed moderate predictability (R² = 0.82), but inconsistent RSLdrop behaviour suggested incomplete or unstable soil removal under suboptimal rinse exposure.
•For extended prerinse durations (>20 min), models showed stronger consistency and higher accuracy (R² = 0.87), indicating that longer exposure times allow for more complete hydration, softening, and dislodgement of fouling layers.}},
author = {{Subramanyam, Aditya}},
language = {{eng}},
note = {{Student Paper}},
title = {{PLANT DATA ANALYSIS OF PRE-RINSE IN CLEANING-IN-PLACE (CIP) PROCEDURES FOR AN ULTRA-HIGH TEMPERATURE (UHT) UNIT OPERATION}},
year = {{2025}},
}