Heat exchangers are crucial components of industrial thermal systems, and their performance decline due to defects like fouling, leakage, and flow blockage can seriously affect operational safety and energy efficiency. This study presents a hybrid intelligent architecture for heat exchanger problem detection, control, and predictive maintenance using industrial process data. The proposed approach combines a Bidirectional Long Short-Term Memory (BiLSTM) network for temporal fault detection, a Fault-Tolerant Model Predictive Control (FT-MPC) strategy to maintain stable operation under fault conditions, and a Hypernetwork Physics-Informed Neural Network (HxPINN) for physics-consistent heat transfer parameter prediction. The models are trained and evaluated using thermal-hydraulic performance deviations that simulate problems like fouling, leakage, blockage, and multiple simultaneous failures. Although the HxPINN model incorporates energy balancing equations into the learning process to estimate heat duty and overall heat transfer coefficient, the BiLSTM model classifies faults. Under unusual circumstances, the FT-MPC controller modifies control inputs and maintains outlet temperature by using the anticipated system states. To assist with maintenance planning, a predictive maintenance framework based on health index and remaining usable life (RUL) estimation is also created. The findings show that the hybrid system increases the accuracy of fault detection, preserves control performance in the face of defects, and permits dependable predictive maintenance. To enable real-time deployment, the entire framework was also implemented in a MATLAB/Simulink environment. The suggested integrated framework offers a reliable method for intelligent industrial heat exchanger system monitoring, control, and maintenance.
Reliable model identification for batch processes is difficult when the measured data contain both repetitive disturbances that recur at similar stages across batches and deterministic disturbances that vary from batch to batch. This study proposes a batch-process identification method that uses input–output data from the current batch and the immediately preceding batch. Differenced signals are formed to reduce the repeated effect of the repetitive disturbance on the output while preserving informative variation for nominal-model identification. A continuous-time nominal process model is then estimated from the differenced data by an integral-transform-based least-squares method. The residual component remaining in the differenced output is represented by Laguerre polynomials, and its coefficients and the model initial states are refined by the prediction error method. The proposed method is evaluated using four benchmark processes representing high-order, non-minimum-phase, and underdamped dynamics, supplemented by measurement-noise analyses and a nonlinear semi-batch case. Under the nominal benchmark configuration, the proposed method produced stable models in all four processes, whereas the previous method did so in only two cases. These results support the use of adjacent-batch differencing under the considered repetitive-disturbance conditions.
The growing reliance on state-of-the-art machine learning based models for engineering decision-making necessitates robust and systematic approaches for model selection. While high predictive accuracy at untrained points is necessary in the final model, other attributes such as model complexity, forecast ability, explainability, scalability, and physics-awareness may be equally important or relevant criteria depending on application context and user requirements. In this work, we propose a novel composite metric called Surrogate Quality Score (SQS) for model selection based on predictive accuracy and model complexity simultaneously. SQS heuristically integrates these two attributes through a weighted product of normalized measures, yielding a unified score to reflect overall model quality. A user-specified parameter in SQS enables explicit control over the accuracy-complexity trade-off, thereby incorporating human judgement and preference in the model selection process. We extensively discuss and justify the formulation of SQS, demonstrate its application through an illustrative example, and examine the sensitivity of its recommendations to user-specified parameters. We further demonstrate its applicability to real-life model selection problems through three engineering case studies. By combining predictive accuracy and model complexity, SQS offers a systematic, objective, and attractive approach for model selection based on a joint consideration of predictive accuracy and model complexity.
This paper proposes a data-driven robust moving horizon state estimation approach for a class of nonlinear systems using a learning-based deep probabilistic stable Koopman model. Different from conventional nonlinear estimation approaches that require the structure or parameter values of a mechanistic model of the underlying nonlinear system, a deep probabilistic stable Koopman operator is learned from noisy data to model the dynamic behavior of the nonlinear system. With the learned Koopman model, a linear robust moving horizon estimation method is proposed for the constrained state estimation of the considered nonlinear system. This method circumvents the challenges associated with non-convex optimization in nonlinear moving horizon estimation and offers an efficient data-based online estimation solution. We provide sufficient conditions that ensure the stability of the developed state estimation scheme in the presence of modeling errors. Finally, the proposed scheme is evaluated using a numerical example and a benchmark simulated chemical process example. The effectiveness and superiority of the proposed scheme are demonstrated.
Carbon dioxide produced in aluminum electrolysis greatly affects molten electrolyte flow, inter-electrode resistance and alumina dissolution, while the sealed high-temperature cell environment hinders real-time bubble monitoring. Traditional CFD simulation of this process requires high-precision meshes and therefore entails huge computational costs. This study adopts physics-informed neural networks (PINN) to combine physical governing equations with CFD data through loss functions, realizing mechanism-data fusion to explore gas-liquid flow characteristics in electrolyte melts. Driven by randomly sampled data, the model accurately predicts full-field internal flow fields using merely 2% of the original data, achieving relative RMSE and MAE both below 10%. Its prediction efficiency is about 3000 times higher than CFD. Simulation results indicate that bubbles drive electrolyte circulation. Rising bubble flow rate raises bubble volume fraction and layer thickness with a slowing growth trend. Bubble-induced extra resistance shows a nonlinear correlation with flow rate, with a critical value of 164.54 L/min; resistance surges rapidly once exceeding this point. Elevated current density also sharply increases extra resistance. Longitudinal-slot anodes can effectively reduce bubble resistance, reaching an 85.33% reduction at 180 L/min. Larger alumina particles present lower mass transfer efficiency and poorer dissolution performance. Rational regulation of bubble flow optimizes particle dissolution. The proposed bubble dynamic optimization method provides practical guidance for energy saving and output improvement in aluminum electrolysis industry.
Plant-wide simulation in biopharmaceutical manufacturing heavily relies on mechanistic models that are costly to develop, while data-driven approaches are typically confined to single unit operations and lack plant-wide integration. This work presents a modular, data-driven modeling framework for plant-wide simulation, where interconnected predictive models represent different sections of the process and enable systematic propagation of predicted outputs and process variables across the entire plant without relying on mechanistic constraints. The framework supports flexible reconfiguration across process topologies and scales, allowing rapid prediction and scenario analysis. A sensitivity analysis is integrated to quantify the impact of intermediate outputs and process parameters on final product yield. A case study on an end-to-end industrial-scale vaccine biopharmaceutical plant, involving 12 manufacturing steps and 665 batches, is used herein to demonstrate the approach. The plant-wide model achieves a yield prediction RMSE of 0.141 and maintains stable predictive performance under ±5% input perturbations. Results reveal highly uneven cross-stage influence, with downstream operations dominating final yield variability. The proposed framework provides a practical and scalable tool for plant-wide modeling and early-stage prediction, as well as a basis for process optimization in biopharmaceutical systems.
Continuous zeolite crystallization in tubular reactors presents major control challenges due to nonlinear kinetics, distributed temperature fields, and multiple heating sources. This work introduces a new thermal model for a pilot-scale continuous oscillatory baffled reactor equipped with conventional and microwave heating for continuous zeolite crystallization. The model extends existing formulations by explicitly incorporating thermostat dynamics and a coaxial microwave heating system, and is validated against experimental data from the pilot plant. Building on this validated process representation, we develop a methodological framework for robust model predictive control (MPC) under uncertainty. High-dimensional partial-differential equation models are replaced by data-driven surrogate models based on nonlinear autoregressive networks with exogenous inputs and dimensionality reduction via principal component analysis and autoencoders. Parametric uncertainty in crystallization kinetics is captured through conformalized quantile regression, providing uncertainty bounds for robust constraint handling in MPC. The resulting framework achieves real-time robust MPC of continuous zeolite crystallization, coordinating multiple heat sources to meet varying throughput targets and product quality constraints while minimizing energy consumption.Closed-loop simulations against a high-fidelity process model show accurate tracking, robustness to uncertain kinetics, and real-time computational feasibility, providing a basis for future deployment on the pilot plant.
The transition toward electrified transportation may reduce the long-term demand for fuel ethanol, creating both challenges and opportunities for the Brazilian bioeconomy. This study evaluates the strategic valorization of ethanol through the production of high-value bioproducts. A multi-criteria mixed integer nonlinear programming model is developed to design an integrated supply chain comprising representative biorefineries, ethanol-derived production plants, maritime hubs, and local and international markets. Economic performance, greenhouse gas emissions, water and energy consumption, and social equity are evaluated across 30 weighting scenarios. The results indicate that export-oriented configurations can generate positive economic returns and support the use of ethanol as a chemical feedstock. Road freight is the main source of modeled emissions, whereas water and energy requirements strongly influence technology selection, production scale, and product portfolios. Under the assumed redistribution mechanism, the model projects population-weighted regional reductions of approximately 0.1-2.4% in the Gini index and 0.2-3.7% in social vulnerability, depending on the region and scenario. Overall, the results demonstrate that, under the adopted assumptions, ethanol-derived value chains can support a sustainable and socially inclusive bioeconomy while providing alternative valorization pathways for ethanol under changing market conditions.
Eco-industrial parks (EIPs) offer significant potential for improving industrial resource efficiency and reducing emissions, yet practical deployment requires transparent monitoring of resource exchanges and verifiable emissions accounting among participating plants. This study presents a framework that enables verifiable emissions and resource tracking in EIPs by coupling a mixed-integer linear programming (MILP) superstructure optimization model with a private permissioned blockchain. The MILP model determines optimal process selection, production capacities, and mass-balanced flows of energy, water, materials, and CO₂ while accounting for Scope 1 and Scope 2 emissions. A binding CO₂ cap is imposed, and alternative compliance pathways are evaluated including capture, utilization, storage, carbon taxation, and credit purchases. Free allowances and credit eligibility are allocated proportionally to baseline emissions, and coalition surplus is distributed using Shapley values. Optimization outputs are subsequently operationalized on a private permissioned blockchain that registers entities, records resource exchanges and emissions reports, and enforces policy rules and financial settlements through smart contracts, thereby providing an auditable link between design decisions and operational outcomes. A case study EIP consisting of seven plants and multiple carbon capture routes demonstrates the framework. Across emission caps of 10 %, 62 %, and 95 % below baseline, optimal profits are 277, 272, and 243 million USD yr⁻¹, with captured CO₂ of 270, 1660, and 1946 kt yr⁻¹, respectively. Under very stringent caps, the system relies on CO₂ credits and carbon taxation to close the remaining compliance gap. The proposed framework generates complete ledgers of resource exchanges and net emissions while maintaining MRV-aligned records suitable for verification, auditing, and settlement. Blockchain energy demand remained negligible for daily to hourly logging and increased materially only under real-time reporting frequencies. The results highlight how explicit policy representation and cooperative profit allocation influence technology selection, trading patterns, and financial outcomes within low-carbon industrial ecosystems.
Chemical process control is essential for safe and reliable operation under disturbances, nonlinear dynamics, and model uncertainties. In this context, data-driven deep reinforcement learning (DRL), particularly the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, has gained attention for learning optimal control policies for continuous processes without explicit models or historical data. This study proposes a Q-value adaptive evaluation TD3 (QAETD3) model to address the challenge of Q-value underestimation, which often arises in TD3 under policy fluctuations and can compromise stability and control performance. In QAETD3, an adaptive evaluation mechanism is developed based on a novel metric that integrates the current Q-value, immediate reward, and next-state Q-value to assess and detect underestimation. An adjusted Q-value is then computed for more stable policy updates. However, as this correction is computed online and varies across samples and training iterations, it introduces time-varying and sample-dependent behavior into the TD target, potentially leading to gradient sensitivity and oscillatory updates under fixed learning rates. To mitigate this, a Triangular Decay Cycle (TDC) learning rate strategy is introduced to dynamically adjust the step size, enhancing convergence and computational stability. The dynamic behavior of the QAETD3 model is described by a system of ordinary differential equations (ODE), and its convergence is verified by applying Lyapunov stability theory and the contraction property of the Bellman operator. The effectiveness of QAETD3 is demonstrated through application to a natural gas dehydration process, where its performance under various disturbances is compared with that of conventional PID and original TD3 controllers.
CFD-based design of hydrogen-capable non-premixed burners is governed by strong trade-offs between thermal performance and NOx emissions. At the same time, each design evaluation can require hours of runtime, and computational cost varies continuously with mesh resolution. We propose a multi-objective, multi-fidelity Bayesian optimization framework that jointly optimizes burner geometry and fuel composition while explicitly managing numerical fidelity and simulation time. Numerical fidelity is represented through a normalized coordinate derived from mesh size and included in an augmented Gaussian-process input space for modeling the mean chamber temperature Tmean and outlet NOx.New candidates are selected by maximizing a composite acquisition function that combines (i) q-log noisy expected hypervolume improvement, (ii) probabilistic feasibility with respect to an emission limit, (iii) a learned fidelity-shaping term, and (iv) a runtime-aware penalty based on an online-calibrated cost proxy. The framework is demonstrated on a confined, axisymmetric non-premixed CH4/H2 burner. Across 238 CFD evaluations, the proposed strategy reduces cumulative computational cost by more than 60% (from 313 to 122 CPU-hours) relative to a hypothetical single-fidelity baseline using only the highest resolution, while recovering a consistent Pareto set of high-temperature and low-emission designs. Flow-field analysis shows that NOx levels are largely dictated by the size and residence time of high-temperature zones, explaining the observed Tmean–NOx trade-off. In addition, re-simulating the best hydrogen-enriched geometry under pure methane yields a different performance ranking, indicating that optimal configurations depend on fuel composition and supporting joint design–operation optimization.
Phosphate ore blending is a chemically constrained decision problem in which contaminant control, grade preservation, sourcing feasibility, and logistics performance must be addressed simultaneously. In industrial practice, blending decisions are often made empirically, which limits the ability to quantify trade-offs between chemical compliance and operational efficiency. This study proposes an optimization-based decision-support framework for phosphate ore blending in a mine-to-plant supply chain under cadmium and P2O5 constraints. The framework combines lexicographic linear programming and multi-objective evolutionary optimization to support blending, sourcing, and transport decisions under stock, capacity, and logistics limitations. In the first stage, a lexicographic linear programming model identifies a chemically rigorous reference solution by sequentially minimizing cadmium content and maximizing phosphate grade. In the second stage, NSGA-II explores the broader set of feasible trade-offs between chemical quality and logistics cost. Computational experiments based on instances reflecting the Tunisian phosphate context show that strict cadmium minimization produces chemically robust but structurally rigid sourcing patterns, whereas controlled relaxation within the admissible chemical region can yield substantial logistics savings while preserving compliance. Beyond the application itself, the results provide an interpretable optimization perspective on chemically constrained blending and highlight the value of combining exact and evolutionary methods for process systems decisions involving quality, logistics, and environmental requirements.
Structural process-model mismatch represents a major challenge in process systems engineering, as it undermines the reliability of mechanistic models, even after precise parameter estimation. Identifying which modeling assumption or grouped terms most contribute to the observed mismatch remains nontrivial. To address this issue, we propose a novel approach based on Sobol’s global sensitivity analysis for diagnosing structural process-model mismatch. This approach is applied to the mismatch trajectory – which is defined as the difference between model predictions and process data – while predefined input channels (e.g., specific functional groups appearing in model equations) are perturbed within uncertainty ranges. Computed sensitivity indices quantify both individual and interaction effects on mismatch variance, thereby providing interpretable diagnostics of dominant structural sources and revealing potential inflation effects due to interactions. The proposed approach is demonstrated through two in-silico case studies: (i) a fed-batch bioreactor for yeast cultivation, and (ii) an ion-exchange chromatography process for biopharmaceutical manufacturing. Results show that our approach can pinpoint structural assumptions driving the mismatch, providing systematic support for model refinement and streamlining the development of more reliable mechanistic and hybrid models for process simulation.
