Digital Chemical Engineering最新文献

The hierarchical decision making in process industries has been traditionally viewed as having a common objective, such as the overall cost, which needs to be optimized. However, a more appropriate approach is to formulate and solve hierarchical optimization and control problems. The solution algorithms for hierarchical optimization problems have been reported in the literature. The idea is to recast each optimization sub-problem in the hierarchy into a multiparametric programming problem, considering the variables of upper-level problems as unknown parameters. In this paper, explicit Model Predictive Control (MPC) and hierarchical optimization techniques, employing multiparametric programming, are combined for hierarchical MPC. The solution algorithm for hierarchical MPC is described in detail. Note that the solution to a hierarchical MPC problem is challenging, even for the simplest case of linear-quadratic objectives. Closed-loop simulations of a thermal mixing process, under two different hierarchical MPC formulations, are performed and the control performance is studied.

Mechanical property prediction plays a crucial role in the steel industry, enabling better materials selection and enhancing production efficiency. In this study, we propose a novel framework that facilitates optimal materials selection for steel’s alloying elements, based on accurate predictions of the Jominy hardness curve. Leveraging Gaussian Process (GP) regression, we provide probabilistic predictions of steel hardenability characteristics from alloy element composition. Taking it a step further, our framework incorporates these accurate predictions into a constrained optimization process, yielding optimal compositions that reduce overall spending while meeting performance specifications. Through data obtained from 1080 steel samples, our GP regression model exhibits high accuracy, achieving an RMSE of 1.37 and showcasing significant improvements in the field. Moreover, our constrained optimization utilizing the GP model and historical market data reveals an average cost reduction of 18% on alloying element expenses, highlighting the tangible cost-saving potential of this approach. By leveraging Gaussian Process (GP) regression, we not only achieve accurate predictions of the Jominy hardness curve based on alloy element composition, but we also introduce a crucial element of uncertainty quantification. This empowers us to place trust in the results of our optimization process, ensuring robust and reliable materials selection. The integration of GP regression and optimization provides a powerful tool for achieving cost-effective materials selection and marks a significant advancement compared to existing studies. This research underscores the promise of machine learning in the steel industry, demonstrating its ability to yield substantial cost savings and enhance decision-making in materials selection.

This work involves the Industry 4.0 infrastructure developed at West Virginia University (WVU) for process systems applications. This infrastructure emulates an interconnected environment, enabling communication and data sharing among different components for use in academic and industrial settings. The current infrastructure encompasses a power plant model interacting with online load demand, distributed control systems, and data analytics components. The developed model of a sub-critical coal-fired power plant is employed to evaluate classical and advanced control strategies using this infrastructure under different operating conditions. Specifically, the control strategies evaluated include classical proportional–integral–derivative (PID) and advanced model predictive control (MPC) structures, focusing on the dynamic matrix control (DMC) approach with an in-house modified sequential quadratic programming (SQP) solver. The MPC approach is developed and simulated in closed loop to address setpoint tracking and load-following scenarios under power plant cycling conditions. In this infrastructure, the PI System centralizes all the information received from the power plant model and the online power demand and sends the control actions calculated by the MPC back to the power plant model for implementation. Results of the implementation of these control strategies are discussed focusing on power plant operating regions associated with cycling.

In the paper, a model for dynamic CFD simulation of BFB boiler furnaces is presented. A CFD model is used in the freeboard region while the bed region is modeled by means of a 0D model. The dynamic model is then applied on a 76 MW BFB boiler furnace to analyse response times to process changes. In the paper, a validation study was first carried out by simulating a known load change situation for which measured heat transfer and oxygen concentration data were available. The model proved to correctly predict the changes. With the validated model, effects of step changes in boiler load and fuel moisture content were then evaluated. According to the model, it takes roughly 30–40 min for the bed to settle to a new steady state. The gas properties after superheaters settle in only a couple of minutes. For the heat transfer to the water and steam side, response time scale is roughly 10 min. The study shows that the developed modeling tool is applicable to analysis of time delays and response times, which are otherwise difficult to analyse in real boilers during normal operation.

There has been a growing interest in Molten Salt Reactors (MSRs) in recent years due to the significant potential for increasing flexibility, security, and reliability of the grid, as well as the inherent passive safety features when compared to traditional pressurized water reactors (PWRs). MSRs can help meet many future nuclear energy goals, such as improved sustainability, high security, high efficiency, and high safety passive features, and help reduce nuclear waste. In this study, to investigate MSRs’ passive safety features, a dynamic model of 9 graphite nodes and 18 fuel salt nodes are simulated in 7 safety scenarios. These simulation results are compared with a traditional PWR dynamic simulation. The simulation shows the stability of MSR operations during these 7 safety scenarios, showing that the coolant and graphite temperature within the system stay within the safety limits of operation. The negative feedback coefficient of the fuel salt within MSR cores plays a significant role in stabilizing the power response inside the core, keeping the power from significant excursions. A one-year simulation is also conducted to test the load-following capabilities of MSRs in comparison with traditional PWRs. It is found that MSRs increase the flexibility, reliability, and security of the grid by operating in load-following mode without the need to change the position of the control rods. MSR's increased efficiency also leads to a reduction in backup fossil-fuel based electricity generation by 82% when compared to traditional PWRs operating in load-following mode.

We present a dynamic risk-based process design and multi-parametric model predictive control optimization approach for real-time process safety management in chemical process systems. A dynamic risk indicator is used to monitor process safety performance considering fault probability and severity, as an explicit function of safety–critical process variables deviation from nominal operating conditions. Process design-aware risk-based multi-parametric model predictive control strategies are then derived which offer the advantages to: (i) integrate safety–critical variable bounds as path constraints, (ii) control risk based on multivariate process dynamics under disturbances, and (iii) provide model-based risk propagation trend forecast. A dynamic optimization problem is then formulated, the solution of which can yield optimal risk control actions, process design values, and/or real-time operating set points. The potential and effectiveness of the proposed approach to systematically account for interactions and trade-offs of multiple decision layers toward improving process safety and efficiency are showcased in a real-world example, the safety–critical control of a continuous stirred tank reactor at T2 Laboratories.

With increasing digitalization and vertical integration of chemical process systems, nonconvex optimization problems often emerge in chemical engineering applications, yet require specialized optimization techniques. Typical global optimization methods proceed by progressively refining bounds on the unknown optimal value, by strategically employing convex relaxations. This article constructs a general closed-form expression for the convex subdifferentials of recent “multivariate McCormick” convex relaxations of nontrivial composite functions, by solving a previous duality formulation in all cases using nonsmooth Karush–Kuhn–Tucker conditions. Based on this subdifferential expression, new automatic differentiation rules are developed to compute gradients and subgradients for multivariate McCormick relaxations, to ultimately generate useful bounds in global optimization. Unlike established differentiation techniques for these relaxations, our new rules are expressed in closed form, do not require solving separate dual optimization problems, are efficiently carried out, and are compatible with the reverse/adjoint mode of algorithmic differentiation. Our formulations become more straightforward when the relevant functions are either smooth or piecewise smooth.

Recent cyberattacks targeting process control systems have demonstrated that reliance on information technology-based approaches alone to address cybersecurity needs is insufficient and that operational technology-based solutions are needed. An attack detection scheme that monitors process operation and determines the presence of an attack represents an operational technology-based approach. Attack detection schemes may be designed to monitor a process operated at or near its steady–state to account for the typical operation of chemical processes. However, transient operation may occur; for example, during process start-up and set–point changes. Detection schemes designed or tuned for steady-state operation may raise false alarms during transient process operation. In this work, we present a reachable set-based cyberattack detection scheme for monitoring processes during transient operation. Both additive and multiplicative false data injection attacks (FDIAs) that alter data communicated over the sensor–controller and controller–actuator communication links are considered. For the class of attacks considered, the detection scheme does not raise false alarms during transient operations. Conditions for classifying attacks based on the ability of the detection scheme to detect the attacks are presented. The application of the reachable set-based detection scheme is demonstrated using two illustrative processes under different FDIAs. For the FDIAs considered, their detectability with respect to the reachable set-based detection scheme is analyzed.

Background

Supervised learning modelling and data-driven optimisation (SLDO) methods have only recently gathered interest in the monoclonal antibody (mAb) platform process development application, but have already demonstrated their advantages over traditional approaches in reducing development costs and accelerating research efforts. With potential usage in multiple unit operations, there is a need for mapping existing SLDO methodologies with the corresponding mAb applications.

Methods

We performed a scoping review of mAb process development studies with at least one SLDO method published prior to April 26, 2022. A team of four independent reviewers conducted a search and synthesised characteristics of the eligible studies from four literature databases.

Results

We identified 30 relevant studies from 1785 citations and 118 full-text papers. 70% were upstream studies (n = 21), and the majority of papers were published between 2010 and 2022 (n = 27, 90%). Multivariate data analysis (MVDA) techniques were identified as the most common SLDO methods (n = 11), and were typically used to model heterogeneous and high-dimensional bioprocess data. While the main usage of SLDO in process development was predictive modelling, a few studies also focused on data pre-processing, knowledge transfer, and optimisation.

Conclusions

Despite the data challenges inherent to the mAb industry, SLDO has been demonstrated to be an efficient solution to some process development use cases such as knowledge transfer, process characterisation, optimisation, and predictive modelling. As biopharmaceutical companies are advancing their digital transformation, SLDO methods will need to be further developed and studied from a more integrative perspective to remain competitive against other platform development approaches.