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. 2024 Nov 16;26:1117–1123. doi: 10.1016/j.reth.2024.11.002

Decreasing electricity costs of clean room for cell products during non-operation

Mitsuru Mizuno a,1,⁎, Koki Abe b,1, Takashi Kakimoto c, Hisashi Hasebe b, Ichiro Sekiya a
PMCID: PMC11614862  PMID: 39635580

Abstract

Introduction

Cell processing facilities are susceptible to environmental bacteria and must maintain sterile environments to safeguard cell products. This process involves circulating air through high-efficiency particulate air (HEPA) filters, which incurs significant maintenance costs. While cost-reduction strategies have been explored in the semiconductor industry, validations specific to cell processing facilities remain unreported. This study aims to verify whether optimizing air-conditioning management in cell processing facilities can achieve energy savings by using particle counters to measure air quality during both non-operational and hypothetical operational conditions.

Methods

The study assessed particle generation under varying air conditions to evaluate potential savings and the impact of reducing air-change rates. The air conditions were defined as follows: Condition 1 (C1) represented normal air conditions (100 %), followed by C2 (72.87 %), C3 (45.74 %), C4 (18.60 %), and C5 (0 %). The number of particles was evaluated across these conditions. Particle counters measured the quantity of particles during non-operational periods and during a 2-min walking motion. The time taken for particle levels to stabilize and become undetectable was also analyzed. Theoretical electricity cost savings were estimated for hypothetical operating and non-operating hours, with calculations adjusted for facilities ranging in size from small (100 m2) to large (1000 m2).

Results

Results indicated that under air conditions C1, C2, C3, and C4, almost no particles were detected, whereas in C5, where air conditioning was halted, particle counts still remained below guideline values. Total particle counts at the four positions were significantly higher at both 0.5 and 5 μm under conditions C4 and C5 compared to other settings. The study also demonstrated that the rate of particle increase during operation varied by air-conditioning condition and position. Notably, reducing the air-change rate significantly enhanced energy savings, especially in larger facilities. For instance, annual electricity consumption in a large facility could potentially be reduced from approximately 31 million yen to approximately 9.6 million yen, yielding savings of approximately 20 million yen.

Conclusions

Even with a reduced air-change rate during non-operation, it was possible to maintain the cleanliness standards for each grade. The findings suggest that current operational practices are often excessive and that significant reductions in operating costs can be achieved by adjusting ventilation frequencies during non-operational periods. This study provides crucial insights for managing cell processing facilities facing challenges such as low production rates, the necessity of operating at full capacity due to on-demand autotransplantation, and high maintenance costs.

Keywords: Clean room, Cell products, Electricity costs, Air-change rate, Contamination risk

Highlights

  • •

    Cell processing facilities operate under environmental and process-derived risks.

  • •

    Maintaining clean environments using HEPA filters involves significant costs.

  • •

    The study verifies energy savings from optimized air-conditioning management.

  • •

    Particle counts were measured during non-operational and operational periods.

  • •

    Reduced air-change rates during non-operational periods resulted in lower costs.

1. Introduction

Cell products are manufactured in cell processing facilities that are vulnerable to numerous risks, including environmental bacterial contamination [1]. These facilities must operate under stringent controls to prevent contamination from both process-derived and environment-derived sources [[2], [3], [4], [5]]. A clean room environment, including an aseptic processing area, is essential to protect cell products from these hazards and must be meticulously maintained and managed according to guidelines such as ISO 13408-1 (2023). Effective management involves grading clean rooms based on factors such as microparticle levels and airborne bacteria concentration [[6], [7], [8]]. Specifically, the critical processing zone in the biosafety cabinet, designated as Grade A, where cells are processed, and the adjacent direct support zone, designated as Grade B, must adhere to specific control values during both non-operating and operating conditions.

Maintaining these clean environments necessitates the circulation of air through HEPA filters [9], a process that incurs substantial air-conditioning and maintenance costs. These costs have become a significant social issue [10,11], particularly in the semiconductor industry, which has experienced considerable growth and innovation in recent years. Consequently, extensive analyses aimed at reducing these costs have been conducted in industrial clean rooms within the semiconductor sector, as well as in biological clean rooms used in pharmaceuticals and other industries [[12], [13], [14], [15], [16], [17], [18], [19]]. These studies have highlighted often excessive clean-air control measures relative to established cleanliness guidelines and have proposed reducing the air-change rate during non-operational periods. However, unlike conventional aseptic goods, cell products cannot be sterilized post-manufacture; instead, their safety is ensured through aseptic manufacturing practices. Thus, reducing the air-change rate solely for cost reduction purposes without proper verification, may compromise the safety of cell products. Furthermore, evaluation regarding cost measures for maintaining cleanliness in facilities that process unique cell products is still insufficient.

The cell processing facilities analyzed in this study are equipped with various instruments for cell culturing, refrigerators for storing culture media, and devices for counting cells. These pieces of equipment contain motors that generate particles and produce heat, causing microparticles to scatter into the clean room. Given this unique environment, it is likely that significant microparticle generation occurs even when the equipment is not operational. In this study, particle counters were installed at several locations within a model clean room to measure particle counts during non-operating conditions and under hypothetical operational conditions, such as 2 min of walking, with reduced air-change rates. The aim of this study was to determine whether optimizing air-conditioning management in cell processing facilities could lead to energy savings.

2. Materials and methods

2.1. Experimental conditions

The study was conducted in a clean room measuring 20.2 m2 and enclosing a volume of 53.4 m3, equipped with four incubators and two biosafety cabinets (SCV-1008EC II A2, Hitachi Industrial Equipment Systems Co., Ltd., Tokyo, Japan). The room featured two fan-coil units (FCUs) with temperature control, one fan-filter unit (FFU) for filtration only, a supply air (SA) system for intake of outside air, and an exhaust air (EA) system vented above the wall (Fig. 1). The SA was delivered as a downflow from a height of 2.7 m from unit F1 (Fig. 1), whereas the EA was expelled through three exhaust ports, each measuring 20 cm × 30 cm, installed at a height of 2.3 m in accordance with the room pressure (Fig. 1). The air intake port in front of the biosafety cabinet was opened 200 mm, and airflow was maintained during both operational and non-operational periods for measurement. To replicate consistent conditions, operators performed a repeated 2-min walk by the same individual. The details of the particle counts are described in the next section. In brief, multiple instruments were used to simultaneously measure particles. The contribution of each air-conditioning unit to the air-change rate is detailed in Table 1. The air conditioners were labeled F1 to F4, and the air conditions, categorized as C1 to C5, were activated by switching each unit on or off (Table 1). Condition C1 (100 %) represented normal air conditions, whereas conditions C2 (72.87 %), C3 (45.74 %), C4 (18.60 %), and C5 (0 %) corresponded to conditions with reduced processing capacity compared to normal conditions. The number of particles measured under each condition was evaluated.

Fig. 1.

Fig. 1

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Experimental conditions. F1, which supplies air, is located on the ceiling, while F2 and F4, which are FCUs, and F3, an FFU, are mounted on the wall. The setup includes two biosafety cabinets and four incubators. Particle counter positions are designated as P1 through P4. P1 is located on the surface of a biosafety cabinet within a Grade A environment, whereas P2, P3, and P4 are situated in a Grade B environment. Data were collected for 30 min during non-operational periods with no operator entry. During operational periods, an operator walked for 2 min, following the indicated walking-motion arrows.

Table 1.

Experimental conditions.

Air condition (operating ratio) In operation Air-change rate Air flow rate (m3/h) Electricity (kW)
C1 (100 %) F1, F2, F3, F4 48.31 4650 15.21
C2 (72.87 %) F1, F3, F4 35.21 3388 5.89
C3 (45.74 %) F1, F2 22.10 2127 1.46
C4 (18.60 %) F1 8.99 865 0.1
C5 (0 %) No operations 0 0 0
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2.2. Particle count analysis

Particle counters were positioned in the clean room to monitor the presence of particles under each air-conditioning scenario. These counters measured particles of 0.5 μm (ZN-PD03-S; OMRON Corporation, Kyoto, Japan) and 5 μm (ZN-PD50-S; OMRON) (Fig. 1). The counters were placed at a height of 80 cm from the floor or the work surface of the biosafety cabinet, marked as positions P1 to P4 (Fig. 1). The particle counter was installed at position P1 in the biosafety cabinet, which was assumed to be a critical processing zone. Position P2, near the biosafety cabinet, served as a particle generation checkpoint within the direct support zone, allowing for the confirmation of normal operation in conjunction with P1. Position P3, located closest to the operational area and directly below the SA, enables early detection of particle generation beneath the point where normal air is supplied. Position P4, situated far from the particle generation area, is utilized to assess the overall effect on the clean room. During non-operational periods, particle data were collected for 30 min. Upon changing conditions, the air conditioning was initially set to C1 with 100 % operation to confirm the absence of particulates before switching to other conditions. Identical data collection periods were employed during operational conditions, which included a 2-min walking motion at a rate of 120 steps per minute, performed upstream of the airflow (Fig. 1). Data were analyzed by comparing particle counts at each position across different air conditions.

2.3. Particle clearance time after walking motion

According to the Japanese Pharmacopoeia, the expected particle clearance time during non-operational periods ranges between 900 and 1200 s (15–20 min) after operation. In this study the time required to clear particles generated by a walking motion under various air conditions was measured, defining clearance as the time taken for particle counts to reduce to five or fewer. If six or more particles were detected, it was classified as particle generation and the clearance time was recorded until particle counts dropped to five or fewer. Condition C5 was excluded from the analysis due to unstable dust generation times.

2.4. Theoretical electricity cost savings

Hypothetical operating and non-operating hours were defined to estimate electricity costs under each air condition. Conditions ranged from C1 to C4 for non-operating hours, assuming air-conditioning units of consistent size. Electricity consumption (kW) per unit is listed in Table 1. Annual electricity usage was calculated for facilities of varying sizes—100 m2 (small), 500 m2 (medium), and 1000 m2 (large)—and priced according to the basic business electricity rate in Tokyo as of January 2024 (1390.87 Japanese yen (JPY)/kW), with seasonal adjustments for summer (23.67 JPY/kW) and winter (22.54 JPY/kW) rates.

2.5. Statistical analysis

Statistical analyses were performed using Prism 9 (GraphPad Inc., La Jolla, CA, USA) and R software (The R Foundation for Statistical Computing, Vienna, Austria). Results are presented as medians with interquartile ranges (IQRs). Each statistical test applied is detailed in the respective figure legends. A P-value of less than 0.05 was considered statistically significant.

3. Results

3.1. Total particles/m3 for 30 min when not in operation

During non-operational conditions, particle counts for both 0.5 and 5 μm sizes were recorded over a period of 30 min. Under air conditions C1 (100 %, 48.31 air changes per hour), C2 (72.87 %, 35.21 air changes per hour), C3 (45.74 %, 22.10 air changes per hour), and C4 (18.60 %, 8.99 air changes per hour), no particles of 0.5 and 5 μm were detected. However, in C5 (0 %, no air changes), a minor increase in particle counts was observed, although still below the guideline values (Fig. 2a). At the four measurement positions (P1 to P4), the count of 0.5 μm particles was notably higher in C5 compared to other conditions, but no significant difference was observed for 5 μm particles (Fig. 2b).

Fig. 2.

Fig. 2

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Total particles/m3for 30 min when not in operation. (a) Total number of particulates per 30 min when not in operation, shown by position. Data are presented as medians with IQRs. Each dashed line indicates the maximum permissible particle count for Grades A and B according to ISO guidelines. (b) Total particle count at four positions under each air-conditioning condition (N = 6). Data are presented as medians with IQRs. ∗P < 0.05. P-values were calculated using the Kruskal–Wallis test with Steel–Dwass's multiple comparison test.

3.2. Total particles/m3 for 30 min during operation

The total number of 0.5 and 5 μm particles was measured over 30 min during operational conditions, which included a 2 min walking motion immediately upon room entry. For air conditions C1, C2, C3, and C4, the number of 0.5 μm particles was under 100 particles/m3, aligning with Grade A standards (Fig. 3a). For C5, the particle counts at P2 (median, 1369: IQR 698.3 to 2015), P3 (median, 1056: IQR 553.5 to 1638), and P4 (median, 980: IQR 432.3 to 1801) exceeded the Grade A level (Fig. 3a). Within the Grade B environment, the particle counts for P2 (median, 97.5: IQR 79.3 to 104.3), P3 (median, 122: IQR 69.0 to 131.3), and P4 (median, 48.5: IQR 44.5 to 56.5) remained below the Grade B level (Fig. 3a). The total particle counts for the four positions at both 0.5 and 5 μm were significantly higher in C4 and C5 compared to other conditions (Fig. 3b).

Fig. 3.

Fig. 3

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Total particles/m3for 30 min during operation. (a) Total number of particulates per 30 min during operation, presented by position. Data are shown as medians with IQRs. Each dashed line represents the maximum permissible particle count for Grades A and B per ISO guidelines. (b) Total particle count at four positions across each air-conditioning condition (N = 6). Data are presented as medians with IQRs. ∗P < 0.05. P-values were determined using the Kruskal–Wallis test with Steel–Dwass's multiple comparison test.

3.3. Particle clearance time after walking motion

Significant variations in particle clearance time were observed across different air conditions and positions following operational activities (Fig. 4a). In P1, located within the biosafety cabinet, neither 0.5 μm nor 5 μm particles were continuously detected under any air-conditioning conditions. For air conditions C1, C2, and C3, particles cleared in under 1200 s, meeting the guideline requirements. In contrast, at positions P2, P3, and P4, clearance times for both 0.5 and 5 μm particles were notably longer under air condition C4 (Fig. 4b and c).

Fig. 4.

Fig. 4

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Time until no particles are detected in operation. (a) Definition of particle count detection. (b) Clearance times for 0.5 μm particles at each position and under each air-conditioning condition (N = 6). Data are presented as medians with IQRs. ∗P < 0.05. P values were calculated using the Kruskal–Wallis test with Dunn's multiple comparison test. Air condition C5 was excluded from this analysis owing to significant skewness in the data, which resulted from the absence of a differential pressure function. Not applicable (NA) was not assigned. (c) Clearance times for 5 μm particles at each position and under each air-conditioning condition. C5 was not included owing to data skewness from the lack of differential pressure function. NA was not assigned.

3.4. Theoretical electricity cost savings relative to the cost of air conditioning

A hypothetical schedule for estimating air-conditioning costs was established, defining operating hours from 9:00 a.m. to 6:00 p.m. and non-operating hours from 6:30 p.m. to 8:30 a.m. the following day, with an additional 30 min preparation period included (Fig. 5a). Electricity energy consumption and costs (JPY) were estimated for air-conditioning conditions C1, C2, C3, and C4 during these non-operating hours. These estimates were calculated based on the electricity usage for facilities of small (100 m2), medium (500 m2), and large (1000 m2) sizes (Fig. 5b). Condition C4, which reduced air-conditioning operation to 18.60 % of full capacity, demonstrated the most substantial energy-saving effect. For a small-sized facility operating under condition C1 (100 %), the estimated electricity consumption was 128,477 kW and approximately 3.0 million JPY per year. In contrast, under condition C4, the estimated consumption was 39,831 kW, costing approximately 0.9 million JPY annually. Similarly, for medium-sized facilities, the reduction in air conditioning from condition C1 to C4 is estimated to decrease electricity usage from 642,384 kW and approximately 15 million JPY to 199,157 kW and approximately 4.5 million JPY per year. For large-sized facilities, a reduction from 1,314,144 kW and approximately 31 million JPY to 407,421 kW and approximately 9.6 million JPY per year was estimated, resulting in potential cost savings of about 21.4 million JPY by adjusting air conditions during non-operational hours (Fig. 5b).

Fig. 5.

Fig. 5

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Theoretical electricity cost savings relative to the cost of air conditioning. (a) Virtual schedule for estimating air-conditioning costs. (b) Estimated annual electric energy consumption (left Y-axis) vs. annual cost of electricity (right Y-axis) for each facility scale under various air-conditioning conditions.

4. Discussion

This study investigated the relationship between air-change rates and particle presence to derive insights for optimizing maintenance costs in clean rooms, which is recognized as a significant social concern, particularly considering costs arising from excessive air-change rates. In conditions C1, C2, and C3, particle levels consistently remained below standard values during both operating and non-operating periods, even though the air-change rate for condition C4 fell below the guidelines. Remarkably, condition C5, with all air-conditioning turned off, also maintained particle counts below the standard thresholds. However, substantial fluctuations in particle counts were noted, raising concerns about the feasibility of maintaining such C5 conditions in actual operations. These observations suggest that current operational protocols might be excessive and that reductions, especially during non-operational periods, are viable. The findings provide crucial insights for managing cell processing facilities, which are characterized by low production volumes, operational challenges due to on-demand autografting, and high maintenance costs.

Many steps in the processing of autologous cell products are yet to be fully automated or digitized, often requiring manual execution [20]. Therefore, multiple operators are involved in the production process, including those who perform operations, record activities, and provide support. The concern that particle counts increase with the number of personnel [1] often leads facilities to maintain an unnecessarily high frequency of ventilation. While the recommended air-change rate is 30 times per hour in Grade B areas (direct support areas) and 20 times per hour in Grade C areas, many facilities operate above these recommendations [17,18]. To determine an appropriate air-change rate, this study generated particles under experimental conditions and evaluated their effects. A primary concern was the risk of particle migration from Grade B to biosafety cabinets, which are open system work areas. Consequently, particle counters were strategically placed to assess impacts on the aseptic work area and adjacent downstream locations. While it may have been beneficial to test the experimental model under more demanding conditions, we consider the model suitable for mitigating risk during non-operational periods. Nevertheless, this experimental model is designed to be adaptable for validation within individual facilities, acknowledging that conditions such as air-conditioning equipment, airflow, and other environmental factors can vary significantly across different sites.

Neither 0.5 nor 5 μm particles exceeded the Grade A and Grade B control values at any sampling points during non-operational conditions. Furthermore, no significant differences were observed across these points. As expected, completely turning off the air conditioning (C5) led to an increase in detections for both 0.5 and 5 μm particles; however, these levels still did not surpass the Grade A and Grade B thresholds. The microparticles detected during the non-operation of the C5 condition were not of human origin, instead they are thought to have been generated by the equipment installed in the clean room. The guidelines specify that particle counts during non-operating conditions should reach the prescribed values within 15–20 min (900–1200 s) after operations cease. In this study, air conditions C1, C2, and C3 achieved a particle count of five or fewer within 20 min after operation. Although this duration extended beyond 30 min in condition C4, compliance with the guideline could be improved by maintaining air conditioning at C1 immediately after operations and then reducing it after a specified period (for example, 30 min later).

Furthermore, the recommended sampling points for clean rooms, as per ISO DIS 14644-1 (2015), are 5–6 for the area analyzed in this study. In our analysis of the Grade B environment, three points were selected for simultaneous measurement. This approach was considered appropriate as it was based on identifying the most risk-prone points in a preliminary study. While no research papers have been published on the sources of particle generation in non-operating clean rooms, the authors’ experience indicates that it is often the motors in refrigerators and incubators that contribute to particle emissions, specifically due to the scattering of lubricating oil used to turn the motors. A more comprehensive analysis that includes identifying sources of particle emissions will be necessary in the future. This study has demonstrated that maintaining a certain air-change rate during non-operational periods ensures that particle levels remain below control values. Reducing the air-change rate during non-operational periods is not only scientifically sound but also constitutes good risk management practice.

Various guidelines indicate that particle measurement in Grade B does not provide clear recommendations for monitoring frequency or optimal sampling points; thus, decisions should be based on risk analysis. In this study, particle counters were strategically placed directly under the walking motion (P3), upstream of the walking motion (P2), and downstream of the walking motion (P4) to assess risk. Contrary to expectations, the analyses in Grade B revealed no significant differences among these measurement points. However, when comparing total values at each measurement point across different air-conditioning conditions, a significant increase in particles was noted at C4 and C5, where the air conditioning was reduced by more than 80 %. Up to condition C3, there was no significant increase in particle counts, suggesting that reducing the air-change rate during operation might be feasible. Supporting this, a previous study reported that while the recommended air-change rate in a Grade C equivalent clean room was set at 20 by the guidelines, data from multiple workers and associated particle and bacterial counts suggested that a rate of 10 was sufficient [17]. However, the presence of microbial particles, which directly impact cell products, cannot be ignored. In this study, 5 μm particles, considered to correlate with falling bacteria [3], were also measured. Under conditions up to C3, 5 μm particles showed no difference; however, a significant increase was observed at C4 and C5. Previous reports indicate that 5 μm particles emitted from clean clothing, such as dust-free garments, contain bacteria that can be cultured on agar media at a rate of 0.08 % [3]. While the quantity of 5 μm particles under C5 conditions was the highest detected in this study, approximately 300, it may be unlikely that bacteria are present in significant numbers in this quantity of particles. However, because airborne bacteria were not simultaneously measured in this study, further verification is required to confirm whether air-change rates during operational periods can be reduced.

It is crucial to emphasize that the most significant direct risk in the production of cell products is not merely the detection of bacteria in Grade B but maintaining the environment in Grade A—where cells are directly manipulated—and preventing bacteria and other microorganisms from entering. Guidelines for clean rooms stipulate that particle analysis for Grade A operations must be conducted at the critical point of operation, specifically within 30 cm of the work area. In this study, particle counters were installed within the biosafety cabinets designated as Grade A to assess the impact of Grade B. The findings revealed that the impact of Grade B was limited, with almost no particles detected in Grade A when airflow was properly maintained. These results suggest that the presence of dust and airborne bacteria in Grade B has a considerably minor impact on the interior of the biosafety cabinet in Grade A during proper operation, which includes preventing blockage of the biosafety cabinet vent and ensuring smooth motion during interventions, such as inserting hands from the side of the biosafety cabinet.

Maintaining clean rooms involves significant air-conditioning costs, contributing to substantial electricity and maintenance expenses that are increasingly recognized as a societal issue [10,11]. Particularly in regenerative medicine, these maintenance costs contribute to high drug prices [21,22]. While highly efficient operational methods for clean rooms are under consideration, a definitive solution remains elusive. The optimization of ventilation frequency during non-operating periods, as explored in this study, suggests potential for significant electricity cost reductions. Maintaining the guideline-recommended ventilation frequency could yield savings of approximately 10 million JPY in a large facility. Furthermore, halving the ventilation frequency during non-operational periods could save an estimated 20 million JPY in large facilities.

Cell manufacturing for regenerative medicine typically experiences low turnover rates owing to its patient-driven, on-demand nature [22]. Despite this, facilities must maintain constant manufacturing capacity to meet production demands, which increases maintenance costs. This study proposes cost-reduction strategies for such facilities without significantly escalating risk. Although direct comparisons are challenging because of variations in equipment and subsequent dust emissions among facilities, the data presented here can be useful for planning and data acquisition in other facilities.

Our research has demonstrated the feasibility of reducing air-conditioning management costs during non-operating hours. However, as automation advances, the concept of ‘non-operating hours’ may evolve, particularly with the increased use of robotic systems for cell culturing. A recent study examined environmental bacteria in a manufacturing environment operated by robots within a clean room [23]. This research found no exceedances of control values for airborne or adherent bacteria, suggesting that predictable robotic movements may allow for a reduction in the overall air-change rate, whether during operation or non-operation.

Nevertheless, it is crucial not to interpret the quantitative data from this study as definitive without proper risk assessment. This research provides foundational information that can enhance the training of operators in cell product manufacturing and support the development of evidence-based control strategies for aseptic production. Ongoing scientific evaluation of new control methods to reduce costs while ensuring safe cell product manufacturing is vital. Such research is expected to advance the dissemination of regenerative medicine technologies, promoting more cost-effective and efficient practices.

Author contributions

MM and KA: Data acquisition, data analysis and interpretation; MM: Manuscript drafting; MM, KA, TK, HH, and IS: Manuscript revision for important intellectual content. All the authors have read and approved the final manuscript.

Funding

This research was funded by a joint research grant from the Shimizu Corporation.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mitsuru Mizuno reports financial support was provided by Shimizu Corporation.

Acknowledgments

We thank Sayaka Komura, Chiaki Okumura, and Hisako Katano for managing the laboratory.

Footnotes

Peer review under responsibility of the Japanese Society for Regenerative Medicine.

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