Historical and contemporary movement and survival rates of walleye (Sander vitreus) in Lake Winnipeg,Canada

Understanding patterns of fish movement in large lake ecosystems is essential for determining appropriate management actions as differences in movement behaviour can influence life history traits such as growth and survival. Lake Winnipeg in Manitoba, Canada supports the 2nd largest walleye (Sander vitreus) commercial fishery in North America. We used mark-recapture models to determine movement and estimate survival of walleye between basins of Lake Winnipeg in historical and contemporary contexts, comparing a tag-recovery study completed historically during 1974–1977 with a contemporary (2017–2019) acoustic telemetry study. Mark-recapture models revealed comparably low but detectable annual transitions between basins from historical (0.3–1.2%) and contemporary datasets (7–8.5%). Historically, fish > 300 mm more frequently moved in a south to north direction. Contemporary estimates suggest similar length-based directionality in that fish > 350 mm were always more likely to move in a south-north direction. Contemporary annual survival derived from mark-recapture models ranged between 27 and 45% and 64.3% when derived from catch curve analysis, while independently derived annual historical survival estimates ranged between 50 and 69% and 45.5% from catch curve analysis. Using the contemporary dataset, we also observed seasonal variation in movement and survival between basins, with the greatest movement across the lake occurring during the fall. Our results demonstrate a persisting pattern of low but measurable movement, suggesting between basin movement is not unusual for Lake Winnipeg. Further, low walleye survival rates reported here for the two time periods studied, support recent management actions to reduce fishing pressure across the lake.     

How Mixing and Light Heterogeneity Impact the Overall Growth Rate in Photobioreactors

The microalgae growth rate in photobioreactors responds with inertia to light stimuli. Here, light variations experienced by the algae are accessed through a coupling of an irradiance field calculation and a Lagrangian particle tracking. The response of algae to fluctuating light is then described by a relaxation model involving a single time constant, the value of which is identified from published data. The overall growth rate is calculated as the sum of individual growth rates of all particles. Instantaneous adaptation and full integration asymptotic behaviors are recovered whilst a finite time constant reveals that the overall growth rate is dependent both on mixing and light distribution. This methodology thus quantitatively relates the design parameters to the photobioreactor performance.     

Advances in the in vitro digestion and fermentation of polysaccharides

In vitro digestion models are widely used to study the structural changes, digestion and release of food components under simulated gastrointestinal conditions. As compared to the in vivo digestion tests, the in vitro digestion reflects the digestion and utilisation of food after ingestion and has the advantages of being time consuming, inexpensive, reproducible and free from moral and ethical restrictions. This study reviewed the current research studies on the in vitro simulated digestion of polysaccharides conducted in the last 5 years and focused on the oral, gastric and intestinal digestion models, with the aim of providing a basis for the further testing of changes in the content, structure and active ingredients of polysaccharides before and after digestion.     

Surface structure and quenching effects in BiFeO3-BaTiO3 ceramics

The structure and properties of Mn-doped 0.67BiFeO₃-0.33BaTiO₃ ceramics are systematically investigated with respect to the effects of annealing prior to rapid cooling by quenching in air. Air-quenching induces a change in crystal structure from pseudo-cubic to rhombohedral, with higher quenching temperatures leading to an increased rhombohedral distortion. These structural changes are correlated with the appearance of more well-defined ferroelectric domain configurations. It is shown that the surface preparation procedures for XRD measurements can induce significant changes in the peak profiles, indicating differences in crystal structure between the surface and bulk regions. Frequency dispersion in the temperature-dependent relative permittivity for the as-sintered sample is significantly reduced after quenching, accompanied by enhancement of the Curie point and improved temperature-stability of piezoelectric properties. It is proposed that the formation of defect clusters by A-site cation diffusion during cooling is circumvented by quenching, leading to the observed modification of structural distortion and ferroelectric properties.     

Integration of machine learning and first principles models

Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white-box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black-box models) and artificial intelligence (AI) techniques. Hybrid models (gray-box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine-learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.     

Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed-integer nonlinear optimization

Large-scale strongly nonlinear and nonconvex mixed-integer nonlinear programming (MINLP) models frequently appear in optimization-based process synthesis, integration, intensification, and process control. However, they are usually difficult to solve by existing algorithms within acceptable time. In this study, we propose two robust homotopy continuation enhanced branch and bound (HCBB) algorithms (denoted as HCBB-FP and HCBB-RB) where the homotopy continuation method is employed to gradually approach the optimum of the NLP subproblem at a node from the solution at its parent node. A variable step length is adapted to effectively balance feasibility and computational efficiency. The computational results from solving four existing process synthesis problems demonstrate that the proposed HCBB algorithms can find the same optimal solution from different initial points, while the existing MINLP algorithms fail or find much worse solutions. In addition, HCBB-RB is superior to HCBB-FP due to much lower computational effort required for the same locally optimal solution.     

A tool for mass transfer evaluation in packed-bed columns for chemical engineering students

This paper presents a Microsoft Excel tool to calculate liquid-gas mass transfer coefficients in packed towers to support numerical design activities in the courses of Unit Operations for Industrial Process and Sustainable Process Design for the Master’s degree in Chemical Engineering of the University of Naples Federico II (Italy).The Mass Transfer Solver Tool (MT Solver Tool) uses several available models to estimate, separately, the values of liquid and gas mass-transfer coefficients and the wet surface area for 144 random and structured packings of interest for absorption/stripping and distillation processes. In addition, a separate spreadsheet can be used in a user-defined mode, to evaluate the mass transfer coefficients with new packing types or to interpret experimental data when the geometrical and physical characteristics of the packing are known. Eventually, the tool is supplied with a data library, where packing geometry and model fitting parameters can be retrieved.The software is aimed to support students and educators in the Unit Operations for Industrial Process and Sustainable Process Design courses. In particular, this is meant to be an example on how the accuracy of design algorithms adopted in unit operation processes is affected by the use of the underpinning correlations for mass transfer rate or pressure drops. Besides, this is aimed to encourage comparison of different correlations when exact field data are not available. Besides, chemical engineers and researchers interested in packed columns design and modelling data may also benefit from the utilization of the software. The MT Solver Tool was introduced to students in a dedicated tutorial lesson after lecturers on packed column design algorithms for distillation, absorption and stripping. Most of the students of the course participated to a group training aimed to simulate the design of an absorption column supported by the MT Solver Tool providing feedback on its application.After the training, an anonymous survey was proposed to the students to monitor the approval rating of the proposed activity and the use of the MT Solver Tool software to support numerical calculations.     

Neural network-based hybrid models developed for free radical polymerization of styrene

In the present work, the free radical polymerization of styrene is modeled by considering the phenomenology of the process (a simplified model, which does not include the diffusional effects, gel, and glass effects) in combination with an empirical model represented by an artificial neural network. Differential evolution (DE) algorithm, belonging to the class of evolutionary algorithms, is applied for developing the neural models in optimal forms. For improving the results—predicted conversion and molecular weights as function of time, temperature, and initiator concentration—different combinations between phenomenological model and neural network are tested; also, individual and stacked neural networks have been developed for the polymerization process. This methodology based on hybrid models, including neural networks aggregated in stacks, provides accurate results.     

Surface modification mechanism of SiC particles using KH5X0 (X=5,6,7,8,9) silane coupling agents: First principle study

Silicon carbide surface modification is still a challenging task. Its modification mechanism is also still unclear. This paper provides a study of the surface modification mechanism of KH5X0 (X = 5, 6, 7, 8, 9) on the silicon carbide (111) using density functional theory. The electronic structures and densities of states of KH5X0 (X = 5, 6, 7, 8, 9) on SiC surfaces indicates that the surface modification mechanism is attributed to the electronic effects of the functional groups of KH5X0 (X = 5, 6, 7, 8, 9). From the results the easier it is for a functional group to obtain electrons, the better the modifying performance of silane coupling agent will be. Furthermore, the interface energy results showed that silicon carbide (111) modification performance by KH580 silane and KH590 silane is better than KH550, KH560, and KH570. The present work provides theoretical guidance for the fabrication of SiC heat sink products.