Dynamic simulation and optimization of anaerobic digestion processes using MATLAB

Time series-based modeling provides a fundamental understanding of process fluctuations in an anaerobic digestion process. However, such models are scarce in literature. In this work, a dynamic model was developed based on modified Hill’s model using MATLAB, which can predict the biomethane production with time series. This model can predict the biomethane production for both batch and continuous process, across substrates and at diverse conditions such as total solids, loading rate, and days of operation. The deviation between literature and the developed model was less than ±7.6%, which shows the accuracy and robustness of this model. Moreover, statistical analysis showed there was no significant difference between literature and simulation, verifying the null hypothesis. Finding a steady and optimized loading rate was necessary to an industrial perspective, which usually requires an extensive experimental data. With the developed model, a stable and optimal methane yield generating loading rate could be identified at minimal input.

Workflow for Biocatalytic Reaction Performance Prediction and Analysis

The development of predictive tools to assess enzyme mutant performance and physical organic approaches to enzyme mechanistic interrogation are crucial to the field of biocatalysis. While many indispensable tools exist to address qualitative aspects of biocatalytic reaction design, they often require extensive experimental data sets or a priori knowledge of reaction mechanism. However, quantitative prediction of enzyme performance is lacking. Herein, we present a workflow that merges both computational and experimental data to produce statistical models that predict the performance of new substrates and enzyme mutants while also providing insight into reaction mechanism. As a validating case study, this platform was applied to investigate a non-native enantioselective photoenzymatic radical cyclization. Statistical models enabled interrogation of the reaction mechanism, and the predictive capabilities of these same models led to the quantitative prediction of the enantioselectivities of new substrates with several enzyme mutants. This platform was constructed for application to any biocatalytic system wherein mechanistic interrogation, prediction of reaction performance with new substrates, or quantitative performance of enzyme mutants would be desirable. Overall, this proof of concept study provides a new tool to complement existing protein engineering and reaction design strategies.

The role of platelets in tumor cell metastasis

Platelets are small, nuclear-free cells whose main function is to stop bleeding. In addition to performing a hemostatic function, platelets are also involved in immune and inflammatory processes. Extensive experimental data suggest that platelets support tumor metastasis and their activation plays a critical role in cancer progression. In the circulatory system, platelets protect tumor cells from immune elimination and promote their arrest at the endothelium, supporting the formation of secondary lesions. Due to the significant contribution of platelets to tumor cells survival and propagation, antithrombotic drugs are considered as a novel anti-metastasis approach. In this article, the authors set a goal to summarize and update the currently existing knowledge about the molecular mechanisms and the role of platelets-tumor cells interaction, as well as to discuss the possibility of platelets receptors as anti-metastasis targets.

Improved UKF-SLAM with Lie Group Operation and Robust Feature Tracking for Motion Vehicles

This paper proposes an improved simultaneous localization and mapping (SLAM) algorithm based on tightly coupled camera images and IMU data, which provides accurate and robust localization for autonomous vehicles and unmanned aerial vehicles (UAV), especially for those in GPS-denied environments. Many research efforts have demonstrated the effectiveness of fusing camera images and inertial data with the Unscented Kalman filter (UKF), but there is still one tricky problem about the non-linearity of the kinematics of rotations. To address this issue, we propose a novel UKF-SLAM approach by rebuilding system and measurement models based on the Lie group and Lie algebra, which obtains state estimates with reasonably high accuracy. Besides, we also offer a new method to handle corner matching outliers, which only causes slightly additional computation costs but eliminates outliers and enhances corner tracking robustness. Results from extensive experimental data have validated the effectiveness of the proposed approach, and this method also achieves comparable precision to the state-of-art.

Dynamic modelling of a servo self-pierce riveting (SPR) process

Self-pierce riveting (SPR) is a complex joining process where multiple layers of material are joined by creating a mechanical interlock via the simultaneous deformation of the inserted rivet and surrounding material. Due to the large number of variables which influence the resulting joint, finding the optimum process parameters has traditionally posed a challenge in the design of the process. Furthermore, there is a gap in knowledge regarding how changes made to the system may affect the produced joint. In this paper, a new system-level model of an inertia-based SPR system is proposed, consisting of a physics-based model of the riveting machine and an empirically-derived model of the joint. Model predictions are validated against extensive experimental data for multiple sets of input conditions, defined by the setting velocity, motor current limit and support frame type. The dynamics of the system and resulting head height of the joint are predicted to a high level of accuracy. Via a model-based case study, changes to the system are identified, which enable either the cycle time or energy consumption to be substantially reduced without compromising the overall quality of the produced joint. The predictive capabilities of the model may be leveraged to reduce the costs involved in the design and validation of SPR systems and processes.

Computation of Acoustic Velocity of Natural Gases With an Alternative Heat Capacity Ratio Equation and Application to Seismic Modeling

We investigate three formulations for computing acoustic velocity of natural gas and derive an equation for the heat capacity ratio, which plays a central role in these formulations. The first formulation is a compilation of fundamental equations available in the engineering literature, referred to as the DASH formulation. The second formulation is a development from the first, in which we use the derived equation for the heat capacity ratio (modified DASH). The third formulation is a mainstream method implemented in Geoscience (BW formulation). All three formulations stem from virial Equations of State that take preponderance in the exploration stage, when the detailed fluid composition is unknown and compositional methods are frequently inapplicable. We test the formulations on an extensive experimental data set of acoustic velocity of natural gases and compare the resulting accuracies. Both DASH and modified DASH formulations provide significantly higher accuracy when compared to the BW formulation. Additionally, the modified DASH, as we derive in this work, has the highest accuracy at pressures above 7000 psi, a condition typically encountered in the Brazilian pre-salt reservoirs. In a final step, we investigate how these different formulations and corresponding accuracies in velocity computation may affect seismic modeling, using a single interface model between a dense gas reservoir and a sealing rock. A direct comparison of amplitude versus offset modeling using our modified DASH formulation and the BW formulation shows up to 50% difference in amplitude calculation in a sensitivity exercise, especially at the longer offsets and higher pressures.

Experimental and field verifications of radial gates as flow measurement structures

Radial gates are common structures in irrigation projects. This paper presents some theoretical-based equations for explicit estimation of the discharge from the radial gate under free and submerged flow conditions using Energy and Momentum (E-M) principles. The proposed equations were calibrated using extensive experimental data collected from the literature and this study for three types of radial gates under free and submerged flow conditions. The submergence threshold of radial gates is concluded, based on the concepts of hydraulic jump and the intersection of free and submerged head-discharge curves. The results indicated that the error in estimating the discharge increases under transition ( − 2.5 ≤ Sr% ≤ + 2.5), gate lip (1 < y0/w ≤ 2), and high submerged (yt/y0 ≥ 0.95) flow conditions. However, in these flow limit conditions, the discharge error can be considerably decreased by adjusting the tailwater depth to flow depth just after the gate and using the energy equation for the sections before and after the gate. The efficiency of the proposed methods was evaluated based on the data series from field measurements of radial gates in 29 check structures at irrigation canals in the United States and Iran. The results showed that the discharge could be estimated using the proposed equations in field conditions with acceptable accuracy.

Liquefaction

Liquefaction of the ground during earthquakes has long been documented and has drawn much attention from earthquake engineers because of its devastation to engineered structures. In this chapter we review a few of the best studied field cases and summarize insights from extensive experimental data critical for understanding the interaction between earthquakes and liquefaction. Despite the progress made in the last few decades, several outstanding problems remain unanswered. One is the mechanism for liquefaction beyond the near field, which has been abundantly documented in the field. This is not well understood because, according to laboratory data, liquefaction should occur only in the near field where the seismic energy density is great enough to cause undrained consolidation leading up to liquefaction. Another outstanding question is the dependence of liquefaction on the frequency of the seismic waves, where the current results from the field and laboratory studies are in conflict. Finally, while in most cases the liquefied sediments are sand or silty sand, well-graded gravel has increasingly been witnessed to liquefy during earthquakes and is not simply the result of entrainment by liquified sand. It is challenging to explain how pore pressure could build up in gravely soils and be maintained at a level high enough to cause liquefaction.

Macroscopic Model of an Abrasive Tool

When designing an abrasive tool, it is necessary to know the most important properties of the abrasive grain-bond system. The greatest interest is the study of the physical and mechanical characteristics of the abrasive tool: hardness, density, bending strength, modulus of elasticity, Poisson's ratio, coefficient of thermal expansion, thermal properties, etc. There are extensive experimental data on mechanical testing of abrasive tools in which a number of empirical formulas have been obtained to determine the physical and mechanical characteristics of an abrasive tool (grain size, hardness, structure, grain and bond grade, etc.). The article is based on core several assumptions and hypotheses that determine the limits of applicability of the results obtained, within which the theory has a complete and complete model of the behavior of the abrasive tool.