设备级生物工艺数字孪生在上游、下游、灌装与生命周期控制中的实用模型

Biopharmaceutical manufacturing depends on complex biological and separation processes that must operate consistently under strict quality expectations. However, traditional controls may not always reveal early trajectory drift, predict when a culture or filtration step will reach an endpoint, warn that a column is approaching a breakthrough limit, or show when equipment behavior differs from a historical batch. A process-development and CMC scientist explores how equipment-level digital twins can help biopharmaceutical manufacturers improve process understanding, batch trajectory monitoring, endpoint prediction, and more.

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Biopharmaceutical manufacturing depends on complex biological and separation processes that must operate consistently under strict quality expectations. Upstream operations such as seed train expansion, fed-batch cell culture, perfusion culture, and microbial fermentation influence product titer, impurity burden, glycosylation, charge variants, aggregation risk, and harvest timing. Downstream operations such as capture and polishing chromatography, viral inactivation and filtration, and ultrafiltration/diafiltration (UF/DF) affect yield, purity, impurity clearance, concentration, buffer exchange, and process robustness. Fill–finish operations then introduce sterile manufacturing expectations, line performance requirements, and container–closure considerations.

Traditional bioprocess control remains essential. Recipes, set points, alarm limits, batch records, in-process testing, laboratory assays, preventive maintenance, deviation systems, and process validation cannot be replaced by models. However, traditional controls may not always reveal early trajectory drift, predict when a culture or filtration step will reach an endpoint, warn that a column is approaching a breakthrough limit, or show when equipment behavior differs from that recorded in historical batch data. A digital twin adds a model-based layer that uses current or recent process data to improve interpretation, forecasting, diagnosis, and decision support (1–6).

Examples are direct and practical. A bioreactor twin may forecast viable cell density (VCD), glucose demand, lactate accumulation, oxygen uptake, or harvest timing. A chromatography twin may predict breakthrough, pooling windows, impurity clearance, or resin-aging effects. A tangential-flow filtration (TFF) twin may detect flux decline, fouling, concentration endpoint, or DF completion. A fill–finish twin may monitor fill-volume drift, stopper or seal trends, reject patterns, or equipment-related interruptions. Such uses support process understanding, quality risk management, operational efficiency, and continued process verification (7–10).

Rather than replacing process scientists, operators, engineers, and quality systems, digital twins are designed to increase the visibility of current process states and to forecast likely behavior before the next sample, alarm, or deviation occurs. We might expect broad enterprise programs to be the most useful early implementations of digital twins. In practice, the best early use cases are focused unit-operation pilots that answer one high-value question reliably.

What Is an Equipment-Level Bioprocess Digital Twin?

An equipment-level bioprocess digital twin is a connected model of a specific unit operation or equipment item. It can represent a stirred-tank bioreactor, perfusion bioreactor, microbial fermentor, chromatography skid, TFF system, viral-filtration setup, single-use mixing system, lyophilizer, or filling line. The twin receives data from a physical operation, applies a model, and produces outputs such as a predicted endpoint, deviation warning, recommended operating window, process-health score, or maintenance alert.

The key difference between a digital twin and an ordinary model is the connection to operating data. An off-line simulation may be used during process development to explore scenarios. A dashboard may display current values. A digital twin, however, links data and a model so that the displayed information reflects the actual process state and can support prediction or diagnosis (Table 1). Figure 1 depicts the general flow of information within a digital twin.

Tool	Purpose	Description
Dashboard	Display data	Shows values for pH, dissolved oxygen, pressure, ultraviolet absorbance, flow rate, viable cell density, and fill weight; does not necessarily predict behavior
Off-line simulation	Study scenarios	Runs a model to study what may happen under selected conditions; may not be connected to routine operating data
Digital model	Represent behavior	Represents the behavior of a process, unit operation, or piece of equipment; may be static or updated only periodically
Digital twin	Mirror and predict process behavior	Connects contextualized operating data with a model so that users can monitor, predict, diagnose, and recommend actions

Table 1: Practical distinctions among dashboards, simulations, digital models, and digital twins

Figure 1: Basic digital twin information flow; PAT = process analytical technology

Digital Twin Architecture: Five Building Blocks

A useful equipment-level digital twin usually has five building blocks: the physical process, the data layer, the model layer, the integration/control layer, and the application layer (Table 2). These blocks do not need to be complex at the start. Many successful implementations begin with one unit operation, a few reliable signals, and a model that supports one defined decision.

Layer	What It Includes	Bioprocess Example	Why It Matters
Physical	Equipment, sensors, actuators, single-use assemblies, skids, and PAT probes	Fed-batch bioreactor with pH, DO, temperature, gas-flow, off-gas, feed-pump, capacitance, and Raman signals	Provides the real-world signals that keep the twin connected to the process
Data	Data acquisition, historian tags, contextualization, batch alignment, calibration status, and storage	Historian and LIMS data linked to batch identification, campaign, cell line, media lot, and instrument status	Turns raw signals and delayed lab results into traceable information
Model	Mechanistic, machine-learning, statistical, or hybrid models	Growth and metabolite model combined with a Raman soft sensor and anomaly detection	Converts data into state estimates, predictions, or recommendations
Integration/control	Interfaces with PLC, DCS, SCADA, MES, LIMS, EBRs, and MPC	Read-only OPC–UA connection for early decision support; later integrated with supervisory control if justified	Allows controlled communication among the twin and plant systems
Application	Dashboards, alerts, workflows, reports, and user decision points	Batch-trajectory screen showing forecasts for glucose, lactate, VCD, titer, and deviation risk	Can be used for routine manufacturing decisions

Table 2: Five building blocks of an equipment-level bioprocess digital twin; DCS = developability classification system, DO = dissolved oxygen, EBR = electronic batch record, LIMS = laboratory information management system, MES = manufacturing execution system, MPC = model predictive control, PAT = process analytical technology, PLC = programmable logic controller, SCADA = supervisory control and data acquisition, OPC–UA = open platform communication–unified architecture, VCD = viable cell density

Digital twins can also be described at different levels. A component twin represents one equipment item, such as a pump, sensor, column, or bioreactor vessel. A unit-operation twin represents a bioreactor run, chromatography step, or TFF operation. A process twin links multiple unit operations, and a site or fleet twin compares similar assets across a facility or network. For most biomanufacturing sites, a practical starting point for implementation is a component or unit-operation twin because scope, data quality, ownership, and validation are easier to define compared with other attributes.

Choosing the Right Model Type

The model is a part of a digital twin that transforms data into a state estimate, forecast, diagnosis, or recommendation. The best model depends on the bioprocess question, available data, biological and physical understanding, expected prediction speed, and regulatory traceability required for the intended use. Three broad model families are common: mechanistic, machine learning (ML), and hybrid models (Table 3).

Model Type	What It Means	Uses	Drawbacks
Mechanistic	Uses equations based on biological, chemical, transport, or equipment understanding	Scale-up, mass balances, oxygen transfer, chromatography transport, filtration resistance, root-cause analysis	Can require difficult parameters and may be too slow for real-time use unless simplified
Machine learning	Learns patterns from historical, experimental, or campaign data	Soft sensors, anomaly detection, endpoint prediction, fault classification, and fast forecasting	Needs representative data and clear controls for extrapolation, bias, drift, and interpretability
Hybrid	Combines mechanistic structure with data-driven correction or estimation	Bioreactor trajectory prediction, Raman soft sensors with process constraints, chromatography pooling, and model-based control	Requires process knowledge, data-science skill, and life-cycle governance for both model components

Table 3: Common model types used in bioprocess digital twins

Practical Model-Selection Guidance: Use a mechanistic model when process physics or biology is reasonably understood and your team needs interpretability, extrapolation, or scale-up insight. Use ML when representative data exist and the goal is fast prediction, pattern recognition, or abnormal-condition detection. Use a hybrid model when mechanistic understanding matters but a purely mechanistic model is too slow, incomplete, or difficult to maintain. Avoid overcomplication. A simple model that is validated for one decision is more valuable than a sophisticated model that users do not trust or maintain.

Applications Across Bioprocessing Unit Operations

Digital twins are most useful when three conditions are present: data are available and reliable, a problem is important enough to justify governance, and a model output is connected to a practical decision. Table 4 provides examples of digital-twin uses in biomanufacturing unit operations.

Unit Operation	Twin Can Predict or Detect	Useful Data Sources	Benefits
Mammalian cell-culture bioreactor	VCD, viability, glucose demand, lactate trend, ammonia, osmolality, oxygen demand, titer trajectory, harvest timing, abnormal batch drift	pH, DO, temperature, agitation, gas flows, off-gas O2/CO2, feeds, base addition, capacitance, Raman/NIR spectroscopy, VCD, metabolites, titer	Better process understanding, earlier intervention, improved feeding strategy, and stronger continued process verification
Microbial fermentation	Substrate limitation, oxygen limitation, heat generation, induction response, productivity trend, foam, pH instability	DO, pH, airflow, agitation, OUR, CER, substrate feed, temperature, off-gas, biomass, product assay	Improved productivity, scale-up understanding, and reduced deviation risk
Perfusion culture	Steady-state drift, cell-retention performance, bleed/harvest balance, membrane or filter fouling	VCD, viability, perfusion rate, cell-specific perfusion rate, bleed rate, metabolites, product titer, pressure, turbidity	More stable long-duration operation and improved response to slowly developing failures
Protein A or affinity chromatography	Breakthrough, loading limit, pooling window, resin-aging trend, pressure rise, cleaning performance	UV, conductivity, pH, flow, pressure, column volume, cycle count, HCP, hcDNA, titer, product quality data	Higher yield, better resin-use strategy, and more robust capture operation
Polishing chromatography	Impurity clearance, charge-variant pooling, aggregate removal, conductivity or pH sensitivity	UV, pH, conductivity, flow, pressure, on-line/off-line HPLC, impurity assays, product-quality data	More consistent product-quality profile and stronger control-strategy understanding
TFF or UF/DF	Flux decline, fouling, concentration endpoint, DF completion, membrane performance drift	TMP, feed/retentate/permeate pressure, flow, conductivity, UV absorbance, concentration, temperature, volume, mass	Reduced fouling, better recovery, fewer endpoint misses, and improved scale-up
Viral filtration	Pressure rise, throughput limit, fouling, flow decay, risk of filter blockage	Pressure, flow, volume processed, protein concentration, turbidity, prefilter performance	Lower failure risk and better batch-interruption prevention
Fill–finish line	Fill-volume drift, reject trends, line stoppage patterns, stopper/seal defects, equipment misalignment	Checkweigher, flow meter, pump signals, machine vision, pressure, event logs, environmental and intervention records	Fewer rejects, earlier detection of drift, and better aseptic process monitoring

Table 4: Examples of digital-twin applications across bioprocessing; CER = carbon-dioxide evolution rate, DO = dissolved oxygen, hcDNA = host-cell DNA, HCP = host-cell protein, HPLC = high-performance liquid chromatography, NIR = near infrared, OUR = oxygen-uptake rate, TFF = tangential-flow filtration, TMP = transmembrane pressure, UF/DF = ultrafiltration/diafiltration, UV = ultraviolet light, VCD = viable cell density

Focused Example: Mammalian Cell-Culture Bioreactor

A mammalian cell-culture bioreactor is a strong candidate for a first bioprocess digital-twin pilot because it is data-rich, biologically important, and directly connected to product yield and downstream burden. In addition to determining whether pH, dissolved oxygen (DO), and temperature are within set points, the twin will need to assess whether batch trajectory is consistent with the expected process state and whether future behavior creates a quality or operational risk.

Practical Objective: The objective of a first bioreactor twin may be to forecast VCD, metabolite trajectories, oxygen demand, feed needs, harvest timing, or deviation risk. The twin does not need to control the bioreactor automatically at first. A valuable preliminary version can provide decision support to an operator, process engineer, or manufacturing science and technology (MSAT) scientist: for example, whether a batch is following the expected golden-batch envelope, whether glucose is likely to fall below a target before the next scheduled sample, or whether lactate reversal will be delayed.

Data and Sensors: Data sources for a bioreactor digital twin can include

primary process data — pH, DO, temperature, agitation, gas flows, overlay/sparge settings, backpressure, feed rates, base addition, antifoam addition, and batch time
cell-culture data — VCD, viability, total cell density, capacitance, osmolality, glucose, lactate, glutamine, ammonia, amino acids, titer, and product-quality attributes (PQAs)
PAT data — Raman, near infrared (NIR), and dielectric spectroscopy; off-gas O2/CO2, mass spectrometry, and other in-line/on-line measurements. Raman and NIR spectroscopy have been demonstrated for simultaneous monitoring of multiple Chinese hamster ovary (CHO) cell-culture variables, with recent Raman studies reporting accurate prediction of metabolites and immunoglobulin G (IgG) titers in 10-L bioreactors (11, 12)
context data — cell line, clone, seed train, media and feed lots, bioreactor scale, single-use assembly, calibration status, operator interventions, campaign number, and deviation history.

Table 5 lists model options and their benefits for use in a bioreactor digital twin.

Model Option	How It Helps	When To Use
Batch-trajectory model	Compares current batch behavior with historical batches and expected operating envelopes	Early decision-support pilot and continued process verification
Soft-sensor model	Estimates variables that are not measured continuously, such as glucose, lactate, titer, VCD, and amino acids	When laboratory measurements are delayed and PAT or high-frequency process data are available
Mechanistic growth and metabolism model	Represents biomass growth, nutrient consumption, metabolite formation, oxygen demand, and feeding responses	When process understanding, extrapolation, and scale-up justification are important
Hybrid model	Combines process equations with PAT, historical data, and machine-learning correction	Routine forecasting when interpretability and prediction speed are needed
Anomaly-detection model	Identifies unusual trajectory patterns, sensor issues, and developing process deviations	Batch monitoring, troubleshooting, and early warning systems

Table 5: Model options for a mammalian cell-culture bioreactor twin

Below is a step-by-step pilot workflow for implementing such a digital twin (Figure 2).

Define the decision. For example, forecast whether glucose will remain within a target range until the next scheduled sample.
Define the user and action.
Map data sources.
Assess data quality.
Build the simplest useful model first. Add mechanistic detail, ML features, or hybrid structure only when needed for the decision.
Validate a model using independent batches or campaigns that were not used for training or calibration.
Deploy the twin as a decision support before using it for automated control or decisions that are critical for good manufacturing practice (GMP) activities.
Monitor life-cycle performance and manage recalibration, retraining, or software updates through approved change control.

Figure 2: Example bioreactor twin workflow; PAT = process analytical technology

Downstream and Fill–Finish Examples

Chromatography Skids and Columns: Chromatography is a strong downstream candidate for digital-twin implementation because it has rich equipment signals, clear process phases, and direct connection to yield and purity. A protein A capture twin can estimate breakthrough risk, pressure rise, binding capacity, column-cycle variability, cleaning effectiveness, or resin-aging trend. A polishing chromatography twin may support pooling decisions, impurity clearance, charge-variant control, and aggregate removal. Digital twins for continuous chromatography have been demonstrated with on-line high-performance liquid chromatography (HPLC) PAT data and empirical or mechanistic models to support real-time pooling decisions for monoclonal antibody purification (13). Connected mechanistic models also have been used to represent chromatography and adjustment steps across a biopharmaceutical downstream process, supporting prediction of manufacturing variability and process robustness (14).

TFF, UF/DF, and Viral Filtration: Filtration operations are practical candidates because pressure, flow, volume, conductivity, and concentration data are usually available at a useful frequency. A TFF twin may estimate flux decline, membrane resistance, concentration endpoint, DF completion, and risk of excessive product exposure. A viral-filtration twin may detect pressure rise, prefilter overload, flow decay, or approaching throughput limits. Such applications are often useful as decision supports because they can prevent batch interruptions or endpoint misses without changing the validated process recipe.

Fill–Finish Equipment: A fill–finish twin may monitor fill-volume accuracy, reject patterns, line stoppages, stopper or seal defects, equipment drift, and leak-detection trends. For sterile products, digital twins should be introduced carefully because aseptic operations depend on a validated contamination-control strategy, environmental monitoring, intervention control, and equipment qualification. A fill–finish twin should strengthen monitoring and process understanding without weakening established aseptic controls (15).

Predictive Maintenance and Equipment Health

Predictive maintenance is one of the most practical early uses of digital twins because it connects equipment-health signals with planned interventions. Instead of maintaining equipment only on a calendar schedule or after failure, predictive maintenance uses data to identify developing degradation. Signals may include vibration, motor current, torque, pressure, valve response, pump speed, flow stability, temperature, cycle count, alarm history, cleaning trends, and reject trends (Table 6).

Signal	Possible Meaning	Example Action
Increasing vibration	Bearing wear, imbalance, or looseness	Inspect during planned downtime and trend against maintenance history.
Rising pressure or TMP	Filter fouling, column blockage, tubing restriction, or viscosity change	Review process conditions, prefilter performance, and equipment condition.
Rising torque or power draw	Agitator wear, mixing-load change, fouling, or material behavior shift	Compare with batch context and determine whether the trend persists.
More frequent alarms	Control instability, sensor issue, component deterioration, or operator-workflow issue	Review alarm history and determine whether maintenance or process action is needed.
Increasing reject rate	Line drift, equipment misalignment, vision-system issue, or component variability	Investigate product, process, and equipment causes.
Longer cycle time	Reduced equipment efficiency, cleaning delay, sensor lag, or process variability	Review root causes and adjust maintenance or operating plan if justified.

Table 6: Examples of equipment-health signals used in predictive maintenance; TMP = transmembrane pressure

In a regulated environment, predictive-maintenance outputs must be linked to a company’s quality system when they affect maintenance timing, batch disposition, validated state, or product-quality decisions. A vibration model used only for engineering awareness may require less formal control than a model that automatically changes a maintenance interval or supports release-impact decisions. The intended use should determine the required level of validation and governance.

Validation, Regulatory Expectations, and Life-Cycle Governance

A bioprocess digital twin should be validated according to its intended use and risk. A twin used only for process-development learning may require less formal control than a twin used for GMP decision support, automated process control, deviation investigation, real-time release support, or batch disposition. Guidances from the US Food and Drug Administration (FDA) and the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) support science-based, risk-based, life-cycle approaches when models, data, and decisions are justified and controlled (7–10).

Because biologics processes are sensitive to raw-material variability, cell-line behavior, scale, equipment configuration, sampling frequency, and analytical-method variability, the validated operating space of a digital twin should be defined carefully. The model should be challenged with independent batches or campaigns representing expected variability. Teams should define what a model can and cannot predict, how uncertainty is displayed, and when users should fall back to established procedures rather than trusting the model output.

Computerized-system expectations also matter. If a twin is deployed in a good practice (GxP) environment, then software, interfaces, data-integrity controls, access management, audit trails, backup/recovery, and change-control procedures should be proportionate to risk. The International Society for Pharmaceutical Engineering’s fifth-iteration good automated-manufacturing practice (GAMP 5) guidance provides a widely used risk-based framework for compliant GxP computerized systems (16). When PAT or spectroscopic models are part of the control strategy, then analytical-procedure validation and life-cycle considerations should also be aligned with ICH Q2(R2) and ICH Q14, as appropriate (17, 18). Table 7 lists examples of questions to ask during digital-twin validation.

Validation Question	Why It Matters
What decision will the twin support?	Validation depends on intended use and patient/product risk.
Who will use the output and what action may follow?	The workflow matters as much as model accuracy does.
What data are used?	Data must be accurate, representative, traceable, contextualized, and protected.
What model is used and why?	Model structure should be scientifically justified and appropriate for the question at hand.
How was the model trained or calibrated?	Training and calibration records support traceability and repeatability.
How was the model tested?	Independent data show whether the model generalizes beyond training batches.
What are the model limits?	Users need to know when predictions are unreliable or outside the validated domain.
How is uncertainty communicated?	Confidence intervals, warning bands, and prediction-quality indicators prevent false precision.
How is model drift monitored?	Performance can change as cells, media, resin, membranes, sensors, or equipment change.
How are updates controlled?	Retraining, recalibration, software patches, and data-pipeline changes should follow change control.

Table 7: Practical validation questions for bioprocess digital twins

Implementation Roadmap for Bioprocess Teams

A digital-twin program should start with a focused bioprocess use case rather than a broad technology initiative. The first project should solve a real problem, use accessible data, fit into routine workflow, and produce a measurable benefit. A practical roadmap is listed below, and Table 8 summarizes common challenges for implementation:

Select a high-value use case, such as bioreactor trajectory prediction, chromatography pooling support, TFF endpoint prediction, viral-filtration risk monitoring, fill-volume drift detection, or predictive maintenance.
Define what decision the twin will support and who will use the output: operators, MSAT scientists, automation engineers, maintenance engineers, quality reviewers, or site leaders.
Map available data sources and confirm data quality, frequency, ownership, calibration status, and traceability.
Choose the simplest model that can support the decision with acceptable accuracy, robustness, and explanation.
Validate the model against independent data and document assumptions, limits, acceptance criteria, and performance.
Deploy first as decision support before considering automated control.
Train operators, engineers, MSAT, quality, maintenance, validation, and data-science teams on how to interpret and challenge outputs.
Monitor model performance and manage changes through life-cycle governance.

Challenge	Mitigation
Poor data quality	Start with data mapping, sensor verification, calibration review, historian review, and clear data ownership.
Weak batch context	Link historian data to batch identification, product, cell line, media lot, equipment configuration, scale, campaign, and sampling events.
Too broad a scope	Begin with one unit operation and one decision. Expand only after the first workflow is trusted.
Model too complex	Use the simplest model that meets the intended use. Add complexity only when doing so improves decisions.
Weak user adoption	Include operators, MSAT, automation, maintenance, and quality users during design and testing.
Validation uncertainty	Define intended use, risk classification, acceptance criteria, and life-cycle plan early.
Model drift	Set performance-monitoring rules, alert thresholds, and retraining triggers.
Integration difficulty	Start with read-only integration where possible and involve automation and IT/OT security early.
Cybersecurity and data integrity	Apply access controls, audit trails, network segmentation, backup/recovery, and change control based on risk.

Table 8: Common implementation challenges and practical mitigations; IT = information technology, MSAT = manufacturing science and technology, OT = operational technology

Future Directions

Digital twins are expected to become more useful as biopharmaceutical sites improve data infrastructure, PAT adoption, automation integration, model governance, and cross-functional digital literacy. Future developments may include stronger hybrid models, physics-informed ML, automated model calibration, multisite equipment twins, federated learning across fleets, augmented-reality operator support, and human–machine collaborative intelligence (5, 6, 19).

The industry needs to move beyond better algorithms, too. Bioprocessing teams need better data context, clearer model ownership, stronger validation strategies, and user interfaces that help operators and scientists understand why a model is making a recommendation. A digital twin is successful when it improves a bioprocess decision in a controlled, explainable, and sustainable way.

Equipment-level digital twins can help biopharmaceutical manufacturers improve process understanding, batch trajectory monitoring, endpoint prediction, deviation detection, predictive maintenance, and life-cycle process verification. The most practical starting point is not a broad digital-transformation program, but a focused use case such as bioreactor trajectory prediction, chromatography pooling support, filtration endpoint prediction, fill-volume drift detection, or equipment-health monitoring.

For biologics manufacturing, digital twins should be developed with quality and validation expectations in mind from the beginning. A model should be appropriate for its intended use, resulting data should be traceable and representative, user workflows should be clear, and changes should be governed throughout a model’s life cycle. When implemented carefully, digital twins can become a practical bridge for bioprocess data, process knowledge, and more reliable manufacturing performance.

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Bhasker Sambar is senior manager of external R&D and technical services at Alvogen Inc., Morristown, NJ, with more than 15 years of experience developing and commercializing sterile injectable and drug–device combination products.

Please cite this article as: Sambar B. Equipment-Level Digital Twins for Bioprocessing: Practical Models for Upstream, Downstream, Fill–Finish, and Life-Cycle Controls. BioProcess Int. 24(9) 2026: 240904.