Proceedings of the MEVTV Workshop on The Evolution of Magma Bodies on Mars

The workshop focused on many of the diverse approaches related to the evolution of magma bodies on Mars that have been pursued during the course of the Mars Evolution of Volcanism, Tectonism, and Volatiles (MEVTV) Program. Approximately 35 scientists from the Mars volcanology, petrology, geochemistry, and modeling communities attended. Segments of the meeting concentrated of laboratory analyses and investigations of SNC meteorites, the interpretation of Viking Orbiter and Lander datasets, and the interpretation of computer codes that model volcanic and tectonic processes on Mars. Abstracts of these reports are presented

Multi-tier framework for the inferential measurement and data-driven modeling

A framework for the inferential measurement and data-driven modeling has been proposed and assessed in several real-world application domains. The architecture of the framework has been structured in multiple tiers to facilitate extensibility and the integration of new components. Each of the proposed four tiers has been assessed in an uncoupled way to verify their suitability. The first tier, dealing with exploratory data analysis, has been assessed with the characterization of the chemical space related to the biodegradation of organic chemicals. This analysis has established relationships between physicochemical variables and biodegradation rates that have been used for model development. At the preprocessing level, a novel method for feature selection based on dissimilarity measures between Self-Organizing maps (SOM) has been developed and assessed. The proposed method selected more features than others published in literature but leads to models with improved predictive power. Single and multiple data imputation techniques based on the SOM have also been used to recover missing data in a Waste Water Treatment Plant benchmark. A new dynamic method to adjust the centers and widths of in Radial basis Function networks has been proposed to predict water quality. The proposed method outperformed other neural networks. The proposed modeling components have also been assessed in the development of prediction and classification models for biodegradation rates in different media. The results obtained proved the suitability of this approach to develop data-driven models when the complex dynamics of the process prevents the formulation of mechanistic models. The use of rule generation algorithms and Bayesian dependency models has been preliminary screened to provide the framework with interpretation capabilities. Preliminary results obtained from the classification of Modes of Toxic Action (MOA) indicate that this could be a promising approach to use MOAs as proxy indicators of human health effects of chemicals.Finally, the complete framework has been applied to three different modeling scenarios. A virtual sensor system, capable of inferring product quality indices from primary process variables has been developed and assessed. The system was integrated with the control system in a real chemical plant outperforming multi-linear correlation models usually adopted by chemical manufacturers. A model to predict carcinogenicity from molecular structure for a set of aromatic compounds has been developed and tested. Results obtained after the application of the SOM-dissimilarity feature selection method yielded better results than models published in the literature. Finally, the framework has been used to facilitate a new approach for environmental modeling and risk management within geographical information systems (GIS). The SOM has been successfully used to characterize exposure scenarios and to provide estimations of missing data through geographic interpolation. The combination of SOM and Gaussian Mixture models facilitated the formulation of a new probabilistic risk assessment approach.Aquesta tesi proposa i avalua en diverses aplicacions reals, un marc general de treball per al desenvolupament de sistemes de mesurament inferencial i de modelat basats en dades. L'arquitectura d'aquest marc de treball s'organitza en diverses capes que faciliten la seva extensibilitat així com la integració de nous components. Cadascun dels quatre nivells en que s'estructura la proposta de marc de treball ha estat avaluat de forma independent per a verificar la seva funcionalitat. El primer que nivell s'ocupa de l'anàlisi exploratòria de dades ha esta avaluat a partir de la caracterització de l'espai químic corresponent a la biodegradació de certs compostos orgànics. Fruit d'aquest anàlisi s'han establert relacions entre diverses variables físico-químiques que han estat emprades posteriorment per al desenvolupament de models de biodegradació. A nivell del preprocés de les dades s'ha desenvolupat i avaluat una nova metodologia per a la selecció de variables basada en l'ús del Mapes Autoorganitzats (SOM). Tot i que el mètode proposat selecciona, en general, un major nombre de variables que altres mètodes proposats a la literatura, els models resultants mostren una millor capacitat predictiva. S'han avaluat també tot un conjunt de tècniques d'imputació de dades basades en el SOM amb un conjunt de dades estàndard corresponent als paràmetres d'operació d'una planta de tractament d'aigües residuals. Es proposa i avalua en un problema de predicció de qualitat en aigua un nou model dinàmic per a ajustar el centre i la dispersió en xarxes de funcions de base radial. El mètode proposat millora els resultats obtinguts amb altres arquitectures neuronals. Els components de modelat proposat s'han aplicat també al desenvolupament de models predictius i de classificació de les velocitats de biodegradació de compostos orgànics en diferents medis. Els resultats obtinguts demostren la viabilitat d'aquesta aproximació per a desenvolupar models basats en dades en aquells casos en els que la complexitat de dinàmica del procés impedeix formular models mecanicistes. S'ha dut a terme un estudi preliminar de l'ús de algorismes de generació de regles i de grafs de dependència bayesiana per a introduir una nova capa que faciliti la interpretació dels models. Els resultats preliminars obtinguts a partir de la classificació dels Modes d'acció Tòxica (MOA) apunten a que l'ús dels MOA com a indicadors intermediaris dels efectes dels compostos químics en la salut és una aproximació factible.Finalment, el marc de treball proposat s'ha aplicat en tres escenaris de modelat diferents. En primer lloc, s'ha desenvolupat i avaluat un sensor virtual capaç d'inferir índexs de qualitat a partir de variables primàries de procés. El sensor resultant ha estat implementat en una planta química real millorant els resultats de les correlacions multilineals emprades habitualment. S'ha desenvolupat i avaluat un model per a predir els efectes carcinògens d'un grup de compostos aromàtics a partir de la seva estructura molecular. Els resultats obtinguts desprès d'aplicar el mètode de selecció de variables basat en el SOM milloren els resultats prèviament publicats. Aquest marc de treball s'ha usat també per a proporcionar una nova aproximació al modelat ambiental i l'anàlisi de risc amb sistemes d'informació geogràfica (GIS). S'ha usat el SOM per a caracteritzar escenaris d'exposició i per a desenvolupar un nou mètode d'interpolació geogràfica. La combinació del SOM amb els models de mescla de gaussianes dona una nova formulació al problema de l'anàlisi de risc des d'un punt de vista probabilístic

Shear wave splitting across the Iceland hot spot: Results from the ICEMELT experiment

We report on observations of upper mantle anisotropy from the splitting of teleseismic shear waves (SKS, SKKS, and PKS) recorded by the ICEMELT broadband seismometer network in Iceland. In a ridge-centered hot spot locale, mantle anisotropy may be generated by flow-induced lattice-preferred orientation of olivine grains or the anisotropic distribution of magma. Splitting measurements of teleseismic shear waves may thus provide diagnostic information on upper mantle flow and/or the distribution of retained melt associated with the Iceland mantle plume. In eastern Iceland, fast polarization directions lie between N10°W and N45°W and average N24°W; delay times between the fast and slow shear waves are generally 0.7–1.35 s. In western Iceland, in contrast, the fast polarization directions, while less well constrained, yield an average value of N23°E and delay times are smaller (0.2–0.95 s). We propose that splitting in eastern Iceland is caused by a 100- to 200-km-thick anisotropic layer in the upper mantle. The observed fast directions in eastern Iceland, however, do not correspond either to the plate spreading direction or to a pattern of radial mantle flow from the center of the Iceland hot spot. We suggest that the relatively uniform direction and magnitude of splitting in eastern Iceland, situated on the Eurasian plate, may therefore reflect the large-scale flow field of the North Atlantic upper mantle. We hypothesize that the different pattern of anisotropy beneath western Iceland, part of the North American plate, is due to the different absolute motions of the two plates. By this view, splitting in eastern and western Iceland is the consequence of shear by North American and Eurasian plate motion relative to the background mantle flow. From absolute plate motion models, in which the Eurasian plate is approximately stationary and the North American plate is moving approximately westward, the splitting observations in both eastern and western Iceland can be satisfied by a background upper mantle flow in the direction N34°W and a velocity of 3 cm/yr in a hot spot reference frame. This inference can be used to test mantle flow models. In particular, it is inconsistent with kinematic flow models, which predict southward flow, or models where flow is dominated by subduction-related sources of mantle buoyancy, which predict westward flow. Our observations are more compatible with the flow field predicted from global seismic tomography models, which in particular include the influence of the large-scale lower mantle upwelling beneath southern Africa. While the hypothesized association between our observations and this upwelling is presently speculative, it makes a very specific and testable prediction about the flow field and hence anisotropy beneath the rest of the Atlantic basin.This work was supported by the National Science Foundation under grants EAR-9316137, OCE-9402991, and EAR-9707193.Peer Reviewe

Applications of Machine Learning to Optimizing Polyolefin Manufacturing

This chapter is a preprint from our book by , focusing on leveraging machine learning (ML) in chemical and polyolefin manufacturing optimization. It's crafted for both novices and seasoned professionals keen on the latest ML applications in chemical processes. We trace the evolution of AI and ML in chemical industries, delineate core ML components, and provide resources for ML beginners. A detailed discussion on various ML methods is presented, covering regression, classification, and unsupervised learning techniques, with performance metrics and examples. Ensemble methods, deep learning networks, including MLP, DNNs, RNNs, CNNs, and transformers, are explored for their growing role in chemical applications. Practical workshops guide readers through predictive modeling using advanced ML algorithms. The chapter culminates with insights into science-guided ML, advocating for a hybrid approach that enhances model accuracy. The extensive bibliography offers resources for further research and practical implementation. This chapter aims to be a thorough primer on ML's practical application in chemical engineering, particularly for polyolefin production, and sets the stage for continued learning in subsequent chapters. Please cite the original work [169,170] when referencing

Improved RBF Network Intrusion Detection Model Based on Edge Computing with Multi-algorithm Fusion

Edge computing is difficult to deploy a complete and reliable security strategy due to its distributed computing architecture and inherent heterogeneity of equipment and limited resources. When malicious attacks occur, the loss will be immeasurable. RBF neural network has strong nonlinear representation ability and fast learning convergence speed, which is suitable for intrusion detection of edge detection industrial control network. In this paper, an improved RBF network intrusion detection model based on multi-algorithm fusion is proposed. kernel principal component analysis (KPCA) is used to extract data dimension and simplify data representation. Then subtractive clustering algorithm(SCM) and grey wolf algorithm(GWO) are used to jointly optimize RBF neural network parameters to avoid falling into local optimum, reduce the calculation of model training and improve the detection accuracy. The algorithm can better adapt to the edge computing platform with weak computing ability and bearing capacity, and realize real-time data analysis.The experimental results of BATADAL data set and Gas data set show that the accuracy of the algorithm is over 99% and the training time of larger samples is shortened by 50 times for BATADAL data set. The results show that the improved RBF network is effective in improving the convergence speed and accuracy in intrusion detection

Dynamic Modelling, Measurement and Control of Co-rotating Twin-Screw Extruders

Co-rotating twin-screw extruders are unique and versatile machines that are used widely in the plastics and food processing industries. Due to the large number of operating variables and design parameters available for manipulation and the complex interactions between them, it cannot be claimed that these extruders are currently being optimally utilised. The most significant improvement to the field of twin-screw extrusion would be through the provision of a generally applicable dynamic process model that is both computationally inexpensive and accurate. This would enable product design, process optimisation and process controller design to be performed cheaply and more thoroughly on a computer than can currently be achieved through experimental trials. This thesis is divided into three parts: dynamic modelling, measurement and control. The first part outlines the development of a dynamic model of the extrusion process which satisfies the above mentioned criteria. The dynamic model predicts quasi-3D spatial profiles of the degree of fill, pressure, temperature, specific mechanical energy input and concentrations of inert and reacting species in the extruder. The individual material transport models which constitute the dynamic model are examined closely for their accuracy and computational efficiency by comparing candidate models amongst themselves and against full 3D finite volume flow models. Several new modelling approaches are proposed in the course of this investigation. The dynamic model achieves a high degree of simplicity and flexibility by assuming a slight compressibility in the process material, allowing the pressure to be calculated directly from the degree of over-fill in each model element using an equation of state. Comparison of the model predictions with dynamic temperature, pressure and residence time distribution data from an extrusion cooking process indicates a good predictive capability. The model can perform dynamic step-change calculations for typical screw configurations in approximately 30 seconds on a 600 MHz Pentium 3 personal computer. The second part of this thesis relates to the measurement of product quality attributes of extruded materials. A digital image processing technique for measuring the bubble size distribution in extruded foams from cross sectional images is presented. It is recognised that this is an important product quality attribute, though difficult to measure accurately with existing techniques. The present technique is demonstrated on several different products. A simulation study of the formation mechanism of polymer foams is also performed. The measurement of product quality attributes such as bulk density and hardness in a manner suitable for automatic control is also addressed. This is achieved through the development of an acoustic sensor for inferring product attributes using the sounds emanating from the product as it leaves the extruder. This method is found to have good prediction ability on unseen data. The third and final part of this thesis relates to the automatic control of product quality attributes using multivariable model predictive controllers based on both direct and indirect control strategies. In the given case study, indirect control strategies, which seek to regulate the product quality attributes through the control of secondary process indicators such as temperature and pressure, are found to cause greater deviations in product quality than taking no corrective control action at all. Conversely, direct control strategies are shown to give tight control over the product quality attributes, provided that appropriate product quality sensors or inferential estimation techniques are available