A Lifecycle Approach to Optimizing Cleaning Systems - Pharmaceutical Technology

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A Lifecycle Approach to Optimizing Cleaning Systems
Clean-in-place systems should be optimized during design and commissioning and after validation.


Pharmaceutical Technology
Volume 36, Issue 12

Automation-system design

The cleaning automation design also should be reviewed for efficient cleaning characteristics. Developing cycles and sequences that complement a particular automation control system greatly reduces long-term operating costs. For instance, a fast response, direct-action, process-logic controller (PLC) may minimize rinse times and water consumption by toggling through every auxiliary path on a complex bioreactor quickly enough in parallel with the sprayballs while not extending sprayball coverage test durations. In contrast, the path transition time within a distributed-control system (DCS) depends on its programming style and may require several layers of equipment module (EM) and sub-EM commands before finally reaching the target control modules (CMs). Only after waiting for valve and state confirmations can the next step begin. Here, creating a cycle that combines multiple transitions into a single grouping will result in the shortest and most cost-efficient cycle possible. Combining cleaning actions (e.g., rinse, drain, and air blow) within phases also reduces the cycle duration. In contrast, a strategy of using more modular, individual phases may elongate the cycle.

Various time and cost-reducing methods must be balanced with skid equipment capability. For example, one may be able to take advantage of integrated PLC capabilities of equipment (e.g., vendor-provided PLC-based centrifuges). These design considerations must be identified early in the project to ensure that quality and validation procedures can be developed to address the sampling, instrumentation, and verification requirements being built into the CIP and recipient systems.

For the CIP cycle itself, the automated step sequencing, step-transition criteria, and parameter values must be well-defined and documented to optimize utility usage while providing sufficient process control.

Sequence. Typically, a cleaning cycle should start with water rinses followed by detergent cleaning and postdetergent rinses. In between any rinse or detergent wash, the system should be drained completely to prevent dilution or chemical reaction with the next cycle step. An air-blow step, placed before the drain, can greatly decrease the gravity drain time and thus decrease the overall cycle time.

Transition criteria. Defining step transition criteria provides a way to control the critical cleaning-cycle parameters. For example, the chemical-wash duration, minimum temperature set point, and concentration target can all be set as requirements before the wash step transitions to the next step.

Parameter values. Laboratory-scale process residue cleaning studies can provide an excellent starting point for CIP cycle parameters. Scalable attributes, such as cleaning-agent concentration, process temperature, exposure time, and external energy, can be explored within the cleaning design space to isolate the most critical parameter(s). Combined with an evaluation of the most effective cleaning agent and identification of worst-case residues in the process, these laboratory-scale efforts can dramatically reduce the number of cycle iterations that must be performed during commissioning and allow for a focus on improving efficiency.

Commissioning

During the commissioning phase, the CIP cycle can be further optimized with hands-on development testing. Knowledge of the CIP step sequence and transition criteria are required to properly set meaningful and efficient parameters for the CIP recipes. Field verification may include monitoring drain points, viewing tank levels during recirculation, or recording pressure readings throughout the supply line.

Development testing can be aided by historical trend analysis of the test cycles. Running cycles and attempting to monitor multiple locations and parameters at the same time may be difficult with limited resources. Flow rate, pressure, temperature, conductivity, and tank-level trends, for example, can be used to optimize specific recipe parameters without having to view each instrument locally while the cycle is running. For instance, when adding chemical to a system during the recirculated wash step, the chemical must be allowed to mix evenly throughout the recirculated solution. The time required to reach the targeted steady-state conductivity can be identified by the historical trends. After comparing multiple iterations through the trends, the mixing cycle-time parameter can be set precisely based on empirical data. This method can eliminate underestimation that causes the wash recirculation to commence with nonuniform solution or overestimation that causes unnecessary cycle-time extension. These data-based decisions help maximize the efficiency of the system before validation, thus locking in cost and time gains.


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