Advanced Control of Continuous Pharmaceutical Manufacturing Processes: A Case Study on the Application of Artificial Neural Network for Predictive Control of a CDC Line.

Jianan Zhao, Geng Tian, Wei Yang, Das Jayanti, Abdollah Koolivand, Xiaoming Xu

Abstract read

In one paragraph

Article in Computers & chemical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jianan ZhaoOffice of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.

Geng TianOffice of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.

Wei YangOffice of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.

Das JayantiOffice of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.

Abdollah KoolivandOffice of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.

Xiaoming XuOffice of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.

Funding

Intramural FDA HHS FD999999

6 · The paper itself

Abstract

The adoption of continuous pharmaceutical manufacturing has driven increased use of modeling, simulation, and advanced process control strategies. Artificial intelligence (AI) model-based approaches, like neural network predictive control (NNPC), offer advantages in providing insights, predictions, and process adjustments. However, evaluating the credibility of such models and accurately quantifying their impact on product quality remains challenging. In this study, a digital twin model of a continuous direct compression (CDC) line was developed based on residence time distribution theory. A two-layer neural network model was trained using data from the digital twin to predict system outputs. The NNPC model combined the trained neural network with an optimization block to adjust control signals and minimize tracking error and control effort. A proportional-integral-derivative (PID) controller was also developed for comparison. The developed neural network model accurately represented the dynamics of the nonlinear system. The tuned NNPC outperformed PID in setpoint tracking (zero overshoot, shorter settling times) and disturbance rejection (≤1.6% peak deviation, settling time of zero) for ±20% and ±50% changes. In conclusion, the NNPC model demonstrated remarkable performance in setpoint tracking and disturbance rejection for the simulated CDC line, underscoring the potential of AI-based control strategies in enhancing product quality and regulatory assessment.

Indexed as

continuous direct compression lineMPCNeural Networkpharmaceutical continuous manufacturingRTD model

Identifiers

PMID41907113

DOI10.1016/j.compchemeng.2026.109560

PMCPMC13019452

What OpenQuestion holds

Textmetadata

LicenceTDM

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.