Canadian Journal of Chemical Engineering最新文献

Non-linear model predictive control (NMPC) is increasingly seen as a promising tool to tackle the problem of handling process nonlinearity and achieve optimal operation. One roadblock to NMPC implementation, however, is the lack of a good model, whether a first-principles-based or a non-linear data-driven-based model such as artificial neural networks (ANN). This manuscript proposes a data-driven modelling approach that integrates an autoencoder-like network and dynamic mode decomposition (DMD) methods to result in a non-linear modelling technique where the non-linearity in the model stems from the modelling of the measured variables. The proposed method results in a semi-linear state-space model where the mapping between the model state and outputs are non-linear (via the autoencoder-like network) while the model dynamics are linear. In the subsequent model predictive controller (MPC) implementation, the autoencoder translates setpoints and outputs to the states of a state space model. The proposed approach is illustrated using a continuously stirred tank reactor simulation example. For comparison, a linear MPC and non-linear MPC based on a traditional neural network (NN) model, a classic Koopman operator-based MPC, and (to benchmark) a perfect model-based NMPC are implemented and tested on several setpoint tracking tasks. The proposed MPC design outperforms the other data driven MPCs, and has similar performance as the first-principles-based NMPC while requiring less computational time and without requiring first principles knowledge.

In the face of the continuous development of novel adsorbents, developing robust adsorbents with high efficiency, strong phosphate selectivity, high regenerability, and cost effectiveness is a scientific challenge. In the present study, an activated carbon-supported MgFe₂O₄-layered double hydroxide (AC@ MgFe₂O₄-LDH) derived Mg–Fe layered double oxide (AC@ MgFe₂O₄-LDO) nanocomposite was synthesized at various temperatures and its potential application for phosphate adsorption was investigated. The nanocomposite exhibited a hierarchical mesoporous structure with a Brunauer–Emmett–Teller (BET) specific surface area of 193 m²/g and a narrow per-size distribution of ~2 nm. AC@MgFe₂O₄-LDO exhibited a high point of zero charge (pH_pzc) value of 9.8 and robust phosphate adsorption potential over a wide pH range of 4–9 due to its high pH buffering capacity. The effects of adsorbent dose, layered double hydroxides (LDH) calcination temperature, initial phosphate concentration, contact time, and temperature on the phosphate adsorption capacity of the adsorbent were investigated. In the present study, up to 99.0% removal of phosphate was achieved at a 4 g/L adsorbent dosage in 4 h at pH 7 and 30°C. An adsorption kinetics study revealed that the adsorption of phosphate by AC@MgFe₂O₄-LDO reached equilibrium within 240 min, with the kinetic experimental data fitting well with pseudo-first-order kinetics (r² >0.99). The Langmuir adsorption isotherm model fit the experimental data well, with a maximum adsorption capacity of 25.81 mg/g. The adsorbent displayed strong phosphate selectivity in the presence of competing anions, and the study demonstrated that AC@MgFe₂O₄-LDO has promising potential for efficient phosphate adsorption over a wide pH range.

The effectiveness of control decisions provided by the safe operation control model for the dense medium coal preparation process may decline due to its inability to adapt to changing working conditions. To address this issue, this paper investigates a safe operation control model update strategy based on Bayesian network and incremental learning. This strategy can update the model structure and parameters according to different conditions, ensuring the effectiveness of the updated model. Considering that the old model has effective information to explain the new working conditions, the Bayesian network structure update learning method based on incremental learning is proposed. This method retains the components of the old model that can describe the joint probability distribution of the sampled data under the new working conditions while updating the remaining structure. This approach improves the efficiency of model updating. The simulation results show that the updated model obtained by the proposed method can effectively deal with new abnormal conditions.

In this paper, we aim to correlate various process and product quality attributes of a mammalian cell culture process with process parameters. To achieve this, we employed physics-informed neural networks that solve the governing ordinary differential equations comprising independent variables (inputs- time, flow rates, and volume) and dependent variables (outputs- viable cell density, dead cell density, glucose concentration, lactate concentration, and monoclonal antibody concentration). The proposed model surpasses the prediction and accuracy capabilities of other commonly used modelling approaches, such as the multilayer perceptron model. It has higher R-squared (R²), lower root mean square error, and lower mean absolute error than the multilayer perceptron model for all output variables (viable cell density, viability, glucose concentration, lactate concentration, and monoclonal antibody concentration). Furthermore, we incorporate a Bayesian optimization study to maximize viable cell density and monoclonal antibody concentration. Single objective optimization and weighted sum multiobjective optimization were carried out for viable cell density and monoclonal antibody concentration in separate (single objective optimization) and combined (multiobjective optimization) forms. An increment of 13.01% and 18.57% for viable cell density and monoclonal antibody concentration, respectively, were projected under single objective optimization, and 46.32% and 67.86%, respectively, for multiobjective optimization as compared to the base case. This study highlights the potential of the physics-informed neural networks-based modelling and optimization of upstream processing of mammalian cell-based monoclonal antibodies in biopharmaceutical operations.

Micro/milli-scale annular reactor with straight and helical forms has excellent heat and mass transfer performance due to the short molecular diffusion distance and dual-wall surface transport. The annular gap spacing is scalable by adjusting the inner and outer tube diameter. The influence of diffusion and convection effects on axial dispersion as expanding the flow scale requires further elucidation with the help of residence time distribution (RTD) curves and Péclet (Pe) numbers. The correlation of RTD characteristics with annulus ratio γ = D_h/D (ratio of annulus characteristic size to outer diameter) is investigated using computational fluid dynamics. Results show that with enlarging the straight annular gap from micro-scale to milli-scale, RTD characteristics exhibit opposing patterns. This can be attributed to the transition from diffusion-dominated to convection-dominated on momentum transfer, and the transition interval is 0.167 < γ < 0.250. Correlation equations of Pe number with Reynolds (Re) number and γ are established under diffusion-dominated and convection-dominated states. The symmetrically distributed secondary flow in the helical annular gap effectively elevates the Pe (Pe_max > 100). Correlation equations of Pe with Re and γ are established in helical annular gaps with 0.083 < γ < 0.208 and 0.167 < γ < 0.500. The above results contribute to understanding the annular flow RTD characteristics for better applications of tube-in-tube reactors.

There is currently a huge imbalance between the demand and supply of freshwater resources. The shortage of fresh water can be mitigated by seawater desalination. Reverse osmosis (RO) is currently the most popular desalination technology around the world. Despite its various advantages, fouling has been one of its major limitations of RO. Membrane fouling can be divided into four categories: colloidal fouling, inorganic fouling, organic fouling, and biofouling. Precipitation of inorganic salts of small solubility, among which CaCO₃, CaSO₄, BaSO₄, and SiO₂ are the most common ones, are the cause of inorganic fouling, which is commonly referred to as scaling. Pretreatment technologies for prevention or mitigation of scaling in the RO process can be classified as conventional pretreatment technologies, which include water softening and scale inhibitors, and membrane-based pretreatment technologies which include nanofiltration, forward osmosis, and membrane surface modification.

When the Smith predictive controller controls the first-order plus dead time process, it is too sensitive to the parameter changes of the system, leading to poor system stability and no practical application value. First, this paper derives and proves the equivalent form of the Smith-proportional-integral-derivative (PID) control strategy as a predictive PI (PPI) control strategy. Second, this paper proposes a new double predictive PI control strategy (DPPI), where the DPPI controller mainly consists of a predictive PI controller and a predictor with an integral link. Again, for the integral predictor in the DPPI control strategy, two new regulation parameters are introduced, which can effectively regulate the control performance and robustness of the control system, improve the degree of freedom of the controller design, and give the principles for the adjustment of the DPPI controller parameters. Finally, it has been verified through simulation experiments that the proposed method can significantly improve response speed and effectively resist external perturbations with good control effect and robust stability.

Typical approaches for assessing the antimicrobial activity of metals-based surfaces involve the contact of a bacterial culture with the surface for a period of time, followed by culturing on agar plates to assess the decrease in microbial viability versus controls. This is a time-consuming methodology requiring at least 24 h to produce a set of results, which can be a bottleneck for productivity in novel materials development. An enzyme-based method was shown to be a satisfactory and much more rapid surrogate test for this application. A β-galactosidase solution was applied to copper, silver, and zinc-based antimicrobial surfaces for up to 1 h, and then the rate of colour development at 578 nm was monitored for a few minutes after addition of the chromogenic enzyme substrate chlorophenol red-β-d-galactopyranoside (CPRG). Highly active antimicrobial surfaces were detected by a lack of colour development, due to enzyme inhibition by the metals. The enzymatic reaction rates were quantified and compared, demonstrating that the copper sample showed the greatest inhibition effect followed by the silver and zinc samples. The antimicrobial activity was quantified using bacteria and the plate count method, and the results correlated well with this enzyme assay, demonstrating that the metals-based antimicrobial activities of both hard and soft (textile) surfaces could be quickly assessed with this enzyme-based methodology.

A computational fluid dynamics (CFD) study of the parameter sensitivity of a wood chips model was performed on an industrial impregnation vessel, which is the first step in a continuous cooking system. The solid and liquid phases were both treated as continua and it was found that the continuum model for the solid wood chips phase could capture the previously observed oscillating formation of arches in the contracting part of the vessel, which will occur at different levels of volume fraction depending on the material constants. The parameters that were examined are the solid pressure, permeability, viscosity, and wall friction. It was found that all the parameters strongly affect the distribution of the wood chips in the vessel as well as the oscillation effects, hence also the flow field which is important to accurately predict in order to ensure optimal performance of the impregnation vessel. Thus, correct material data for these types of simulations are crucial to the outcome and should be chosen for the appropriate situation and bio-material.