A quality by design approach to process plant cleaning

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Abstract

The cleaning of process plant has traditionally been an activity that has been carried out in open-loop mode, with confirmation of cleanliness achieved through off-line sample assessment. Such strategies have partly arisen as the depth of scientific understanding of the cleaning process has been limited. With deeper understanding through the tracking and prediction of cleaning progression, more sophisticated approaches can be adopted allowing the timely termination of cleaning operations. This paper discusses the component needs of the improved system. At its heart is the need to use appropriate measurement devices for the soil of interest to measure the current process condition and to derive predictive strategies to specify when to terminate cleaning. Results from a case study application on the cleaning of a toothpaste pilot plant demonstrate the concepts. The use of spectroscopic measurements is contrasted with more traditional measurements such as turbidity to track the cleaning profile. Improvement is not achieved simply through better measurement, algorithmic methods for measurement enhancement and forecasting to predict end point of cleaning are both necessary in order to achieve the termination of cleaning operations in a timely manner. The capability to perform both these tasks is considered using the experimental cleaning case study.

Highlights

► The ability to track process cleaning and gain greater insight into the progression of cleaning. ► The measurement of cleaning and insight gained into the variability associated with cleaning. ► The ability to forecast the cleaning end-point from early in the cleaning period when measurements of cleanliness are available.

Introduction

Considering the relative time involved in process cleaning compared with operation, it is surprising that process cleaning is a subject that has attracted limited academic interest, with the possible exception of studies in the dairy industry (Wilson, 2005). Batch process operation, the predominant production strategy in many process sectors, by its very nature requires the cleaning of plant between batches. The extent of soil removal required depends upon the form of operation and the consequences of batch-to-batch carry over, with multi-product batch plant generally having higher soil removal requirements than plant that is within a single product line. Whatever the cleanliness level required, there is generally a need to implement a cleaning operation between batch cycles. For processes subject to validation requirements, the general issues relating to cleaning and its validation in regulated environments are described in FDA Guidance to Inspectors (1993). Included in this are outlines of what is required in Standard Operating Procedures, validation of process cleanliness and measurement issues and considerations regarding the setting of cleanliness limits.

The design of a cleaning system begins with the original process equipment design. This paper does not consider these aspects as the majority of industrial opportunities arise in improvement of current plant although clearly a plant designed at the outset with ‘cleanability’ as a critical design parameter is the preferred option. The typical approach to process cleaning involves carrying out a series of plant trials of the clean in place (CIP) system during which process cleaning parameters (e.g. flowrates, temperatures, cleaning agent type and concentration) are varied. Off-line analysis of process swabs is used to determine whether the process is clean and CIP parameters selected which lead to consistently clean plant chosen. Following this, the cleaning strategy becomes fixed with the same CIP parameters used time after time. Off-line confirmation of plant cleanliness is periodically tested using swabbing, with the frequency of these tests dependent upon the criticality of success the cleaning operation. The precise levels of concentration that form the clean/soiled threshold are product specific but are defined in each case by standard operating policies.

Previous cleaning related research has considered the nature of the soil and its impact on strategy, fundamental science of soil adhesion and removal, modelling of the cleaning of process plant and improvement in strategy. A broad view, considering all four aspects, was provided by Wilson (2005) who highlighted current limitations in mechanistic understanding that need to be addressed if improvements in cleaning are to be achieved. Challenges in identifying appropriate instrumentation and its use to determine in a reliable manner end point were identified as critical considerations. Along with this, the need to understand the nature of the fouling deposit was also highlighted as the first step in improving a cleaning strategy. To this end, Fryer and Asteriadou (2009) developed a cleaning map where the nature of the soil was related to the cleaning approach taken. In moving towards improved cleaning, they highlighted the need to develop mechanistic understanding and descriptions of soil removal as a first step in the long-term goal of predictive modelling. Although they do raise the concern that such descriptions may need to account for stochastic effects. Liu et al. (2006) undertook a series of experimental investigations using micromanipulation to assess cohesiveness of soils and from that developed simple models for removal at the surface.

While mechanistic level descriptions of surface behaviour are important, from an industrial perspective, being able to track cleaning of process unit operations and their inter-linking pipework are of prime importance. Prosek et al. (2005) considered cleaning of pipes and how to predict cleaning rates as pipe geometry and layout changes. The approach adopted is conceptually similar to that used to determine pressure drop in pipes with equivalent length used to account for bends, valves etc. Pipe cleaning was also considered by Lelièvre et al. (2002) but in this case the removal of bacterial contamination along with soil was assessed. They developed models describing the relationship between cleaning rate and process operational parameters such as flowrate and chemical composition. In their analysis they drew on the work of Dürr (2002) who developed models for cleaning of milk heat exchangers and quantified dynamic behaviour and its relation to soil type. Importantly Dürr extended previous models that had been formulated for specific case studies, to a more general model that accounts for alternative mechanisms for soil removal. However, the work described considered only the modelling of milk cleaning from heat exchangers, leaving open the question of applicability to other systems and importantly the use of the model to improve the cleaning operation.

In analyzing cleaning systems, it is important to fully appreciate the costs associated with the operation. Alvarez et al. (2004) analyzed the operating costs of the cleaning operation and how water usage could be optimized. An assessment of costs of operation is vitally important in making decisions regarding improvement projects but the challenge of where to set the boundary of consideration is important. Increases in plant availability and reduction in cleaning costs is obvious, but at the other extreme accounting for carbon emissions and associated taxes can be more complex. This paper does not attempt to make a full financial analysis of the business case but it is worth recognizing the complexity of the improvement decision that needs to be made. In making the decision to invest in improvement, several established techniques can be drawn on. Ahmad and Benson (2000) sets out a comprehensive approach for comparative assessment of process performance against what is achieved in comparable cases. This provides a high level comparison of the operational capability. At the control loop level the methods of cost benefit analysis (Anderson, 1996) are applicable to financial decision making. Both the approaches tackle the fundamental issue of how improvement can be estimated before investing in the project to make the improvement.

In operational terms, predictive models are an essential component of many optimization and control approaches. In cleaning, previous research has thus demonstrated that models of the behaviour of soils at the surface of process plant can be developed. A universally applicable model structure has yet to be constructed, progress towards which is hampered by the variety of removal mechanisms that exist. While such scientific insight is invaluable, from a process operational perspective predictive models that determine cleaning rates of whole process units and relate these to process parameters such as flowrates and temperatures are more directly exploitable than descriptions of localized surface behaviour. The question then remains is how can such models be exploited for industrial benefit and do the necessary process operational modifications they indicate justify the expenditure involved. It is these aspects that this paper considers, firstly the strategy for improvement and then the instrumentation and control approaches that allow the strategy to be materialized.

Section snippets

The cleaning strategy

The traditional CIP strategy is essentially open loop as there are no changes in operational policy in response to process cleaning performance. Diagrammatically this is shown in Fig. 1 where the open loop policy is set so that virtually all process variation is accommodated in normal operation, i.e. the natural spread of CIP performance results in cleanliness levels that do not violate cleanliness targets. The broad width of distribution is as a consequence of natural variation. The mean final

Analysis of experimental results

The first task in analysis was to get a comparative indication of cleaning time as the process conditions vary. A fundamental measurement need therefore is the ability to track the progression of cleaning through analysis of water leaving the pilot plant. The assumption is that once the concentration in the wastewater falls to what can be considered close to zero then the process is clean. While the quantification of what is close to zero is product and process specific, it follows that there

Forecasting of cleaning progression

The results shown in Section 3 above indicate that in the case of toothpaste it is possible to track the concentration in the effluent down to levels that are indicative of a clean process. This situation will not arise for all soils and in many cases measurement limitations will dictate that it will be necessary to gain an on-line indication of cleaning end-point from a forecast of the progression of clean in the measurable region. The toothpaste case study provides an ideal test-bed of this

Conclusions

This paper has raised the prospect of a new paradigm in process cleaning in the move from open loop control that is not responsive to process behaviour to an end-point detection philosophy where natural variation is taken into consideration. The traditional open-loop operation also fails to allow an appreciation for the response to variation of operational parameters to be gained. Through variation of CIP parameter settings an appreciation of the impact on cleaning times has been acquired.

Acknowledgements

The authors would like to acknowledge the financial support of the UK Technology Strategy Board. The contribution of the members of the ZEAL project consortium is also acknowledged for their development of the pilot plant at the University of Birmingham and discussions regarding process plant cleaning and best practice assessment. In particular, thanks go to Dr Konstantia Asteriadou for her considerable contribution to ensuring the functionality of the pilot plant.

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