in-line optimization cleaning in place (cip)

OptiCIP+: In-line monitoring, optimisationand control of cleaning in place

A. J. van Asselt, D. Allersma, F. Smit, G. van Houwelingen and P. De Jongarjan.van.asselt@nizo.nl

AbstractThe current way of CIP cleaning is in most cases

based on experience and often longer than strictly

necessary due to the lack of reliable monitoring

tools. Currently, NIZO food research is developing a

real-time monitoring system, based on sensors and

process simulation tools. This will ensure a further

reduction of cleaning costs and an increase in

production time.

IntroductionCleaning in Place (CIP) is an important unit-

operation that is commonly applied throughout the

whole food industr y. In the dairy industry, CIP is

applied daily for the major part of the equipment.

This ensures a constant product quality, efficient

heat transfer in heat exchangers and avoids possible

growth of micro-organisms. It is clear that a high

frequency of cleaning has a large impact on the

availability of the processing equipment and on the

environment (ennergy use, product loss) . If cleaning

times can be reduced, production capacity will

increase without additional investments. In

addition, energy savings and reduced product loss

can be obtained. An evaluation study in the industr y

showed that CIP procedures are based on worst case

scenarios and there is a potential for 30% saving in

cleaning efficiency.

An objective analysis is needed to evaluate whether

reduction of cleaning time is possible without

affecting product quality and safety.

OptiCIP+In order to answer this need, NIZO food research has

developed a model that is able to optimise the CIP

procedure in a food factor y. By using that CIP model

in combination with a model based process control

system (Premic), a tool is available to monitor,

optimise and control the CIP procedure. Optmisation

of cleaning prcedures is carried out in three steps:

1. Based on the actual production data, the type

of product and the temperature difference the

amount of fouling is calculated.

2. After production, a database file is searched

and depending on the degree of fouling , an

initial CIP procedure is loaded into the system.

3. During cleaning all CIP related data is linked

with the production and product composition

data and stored in a database file.

By making use of the sensor values, the CIP

procedure will continuously be optimized during

cleaning. A schematic overview of OptiCIP+ and

the different steps of the process is shown in Figure 2.

ResultsOne of the first targets within this project was to

find the right in-line sensor systems to monitor the

fouling removal. The results of the fouling removal

monitored by this in-line turbidity sensor and an

off-line turbidity device are shown in Figure 3.

Figure 3: Turbidity in-line versus off-line during

cleaning

The results show that the in-line measurement of

turbidity is very well comparable with the off-line

measurement.

The in-line measurement of calcium is currently

under investigation.

Cost reductionApplication of OptiCIP+ can result in significant cost

savings for the dairy industry. For a company with

500 000 tons of milk processing preliminary

calculations showed when the cleaning time is

reduced by 25% and fouling is reduced by 5%, annual

savings up to 200 000 can be possible. With regard

to the environmental aspects 9000 GJ of energy can

be saved and the product loss can be reduced by 550

tons.

AcknowledgementThis work has been supported by the Dutch Dairy

Association, Honeywell and the Netherlands Agency

for Energy and Environment (SenterNovem).

ConclusionNIZO food research has developed a system, called OptiCIP+, that makes it possibleto optimize the CIP procedure in-line.By using OptiCIP+ the food industry can achieve a significant cost reduction byshorter cleaning times and higher production capacities of up to 10-20%. Thesystem is currently being demonstrated in the dairy industry.

Figure 2: Principle OptiCIP+

Figure 1: Cleaning efficiency before and afteroptimisation based on fouling removal

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