Process Control and Simulation

Process control and simulation lie at the heart of modern industrial operation, uniting mathematical models, real‐time measurements and optimisation algorithms to maintain safe, efficient and profitable production. By emulating plant dynamics in virtual environments—so‐called digital twins—engineers can explore control strategies, train operators and validate new schemes before they are applied in the field. Core techniques range from classical PID loops augmented with dynamic simulations to advanced model‐based controllers, including model predictive control, robust and adaptive schemes, and data‐driven soft sensors. Simulation tools capture nonlinearities, delays and multivariable interactions, allowing rigorous testing under disturbance scenarios and transient events. With the global drive towards decarbonisation, process control and simulation have expanded into energy systems coordination, integrated multi‐energy networks and real‐time scheduling of renewables and storage. In bioprocessing, digital models of fermenters and reactors support closed‐loop soft sensing and state estimation, boosting product quality. Across sectors—chemicals, power, water treatment, pharmaceutical and beyond—the fusion of control theory with high‐fidelity simulation underpins smarter automation, predictable maintenance and resilient operation in the face of uncertain supply, market and environmental conditions.

Research from Nature Portfolio

An early‐warning and control framework has been developed for coupled gas–electric networks, in which pressure anomalies in a high‐pressure natural‐gas delivery system are detected rapidly and conveyed to generation dispatch algorithms. By relaying malfunction indicators faster than the physical decay of pipeline pressure, adaptive redispatch of gas‐fired power units can pre‐empt cascading blackouts, incorporating simulation of both fluid and grid dynamics. In biochemical process control, a local selective‐ensemble soft‐sensing strategy has been formulated for penicillin fermentation. From transfer‐entropy‐based data localisation and multi‐objective regression, an ensemble of support‐vector‐regression submodels is assembled with adaptive weighting, yielding accurate reconstruction of unmeasurable biomass and product concentrations under dynamic feed and disturbance profiles. Complementing these data‐driven estimators, a differential‐geometry analysis of a non‐linear continuous‐stirred tank reactor (CSTR) has established rigorous observability and detectability criteria under parametric uncertainty. By treating unknown inputs and initial conditions as indistinguishable disturbances, classical tools have defined which measurement combinations permit unique state reconstruction and informed the design of robust state observers for experimental reactors.

Research from all publishers

A deep reinforcement‐learning (DRL) methodology has been shown to manage strongly coupled multivariable loops. Using proximal policy optimisation with normalised advantage signals and custom reward functions, coupled chemical and energy process models were stabilised under varying setpoints and disturbances, outperforming classical decentralised PID and decoupled controllers in both transient response and disturbance rejection. In the energy domain, two‐stage distributionally robust optimisation (DRO) has been applied to multi‐energy systems with hybrid electricity–hydrogen storage. By decomposing day‐ahead scheduling and intraday rescheduling under worst-case distributional uncertainty, the resulting mixed‐integer linear programme balances conservativeness with robustness, yielding cost‐effective and emission‐aware dispatch across electrical, thermal and gas networks. Finally, a mixed‐integer‐programming framework has been introduced for soft‐sensor training that simultaneously selects input variables and filters outliers. By formulating variable selection and data cleansing as a single optimisation problem, and applying piecewise linearisations for global optimality, robust models of latent quality variables were trained more efficiently and with improved prediction performance across multiple industrial datasets.

Process Control and Simulation publication trend

The graph below shows the total number of articles in process control and simulation across all publications each year (not limited to Nature Index journals).

Technical terms

Digital twin: A dynamic computational model of a physical process, updated in real time to mirror plant behaviour for analysis and control design.

Soft sensor: A data‐driven or grey‐box estimator that infers unmeasured process variables from available sensor data.

Observability: A property determining whether the internal states of a dynamic system can be uniquely reconstructed from output measurements over time.

Distributionally robust optimisation (DRO): An optimisation framework seeking solutions that remain effective under the worst‐case probability distributions within prescribed ambiguity sets.

Deep reinforcement learning (DRL): A control approach combining deep neural networks with reinforcement‐learning algorithms to develop policies for decision‐making in complex, uncertain environments.

References

1. Early warning and proactive control strategies for power blackouts caused by gas network malfunctions. Nature Communications (2024).
2. Soft sensing modeling of penicillin fermentation process based on local selection ensemble learning. Scientific Reports (2024).
3. An observability and detectability analysis for non-linear uncertain CSTR model of biochemical processes. Scientific Reports (2022).
4. Multivariable Coupled System Control Method Based on Deep Reinforcement Learning. Sensors (2023).
5. Two-stage distributionally robust optimization-based coordinated scheduling of integrated energy system with electricity-hydrogen hybrid energy storage. Protection and Control of Modern Power Systems (2023).
6. A Mixed-Integer Formulation for the Simultaneous Input Selection and Outlier Filtering in Soft Sensor Training. Information Systems Frontiers (2024).