Development of surrogate models for distillation trains

El temps d’execució necessari per a la resolució de problemes d’optimització en programes de simulació rigorosos no sol ser asequible, fet que promou l’ús de models de substitució. El desenvolupament d’aquests models aproximats comporta la resolució d’una sèrie de reptes com la càrrega computacional i el risc d’excés d’adequació del model. En el treball presentat, les eines i procediments per a crear, entrenar i validar una xarxa neuronal (ANN) son desenvolupats per a l’entrenament de models de simplificació de simulacions rigoroses. Les eines proposades han estat posades a prova en un cas d’estudi que aborda la síntesis de trens de separació per als productes de la pirólisis del polietilé, centrant-se en les columnes de destil·lació del procés simulades en Aspen-HYSYS. Finalment, dos models ANN que simulen el comportament de la columna respecte una funció que considera els costos de la simulació han estat desenvolupats. El comportament i precisió dels dos models és correspon a l’estudiat en la superfície triada.El tiempo de computación necesario para solucionar problemas de optimización en programas de simulación rigurosos no suele ser asequible, lo que promueve el uso de modelos de sustitución. El desarrollo de estos modelos aproximados conlleva la resolución de una serie de retos como la carga computacional y el riesgo de sobreajuste del modelo. En el presente trabajo, las herramientas y procedimientos para crear, entrenar y validar una red neuronal artificial (ANN), han sido desarrollados para la construcción de modelos simplificados de simulaciones rigurosas. Las herramientas propuestas han sido puestas a prueba en un caso de estudio que aborda la síntesis de trenes de separación para los productos de la pirolisis del polietileno, centrándose en las columnas de destilación del proceso simuladas en Aspen-HYSYS. Finalmente, dos modelos de redes neuronales que simulan el comportamiento de la columna con respecto a una función que considera los costes de la simulación han sido desarrollados. Los dos modelos representan correctamente y con buena precisión la superficie estudiada.The computational time required to solve optimization problems in rigorous simulation programs is usually unaffordable, raising the need to use surrogate models. The development of these approximate models is a challenge that needs to handle the computational burden and risk of over fitting. In the present work, tools and procedures to build, train, and validate an Artificial Neural Network (ANN) are developed to build simplified models of rigorous simulations. The proposed tools are tested with a case study that addresses the synthesis of separation trains for the products of polyethylene pyrolysis, focusing in the distillation columns of the process simulated with Aspen-HYSYS. Finally, two ANN models have been developed to simulate the behaviour of the column regarding a function that considers the costs of the simulation. Both models fit correctly and show good accuracies with respect the surface studied

Implementation of an innovative teaching project in a Chemical Process Design course at the University of Cantabria, Spain

This paper shows the planning, the teaching activities and the evaluation of the learning and teaching process implemented in the Chemical Process Design course at the University of Cantabria, Spain. Educational methods to address the knowledge, skills and attitudes that students who complete the course are expected to acquire are proposed and discussed. Undergraduate and graduate engineers' perceptions of the methodology used are evaluated by means of a questionnaire. Results of the teaching activities and the strengths and weaknesses of the proposed case study are discussed in relation to the course characteristics. The findings of the empirical evaluation shows that the excessive time students had to dedicate to the case study project and dealing with limited information are the most negative aspects obtained, whereas an increase in the students' self-confidence and the practical application of the methodology are the most positive aspects. Finally, improvements are discussed in order to extend the application of the methodology to other courses offered as part of the chemical engineering degree.This work was partially supported with the financial help of the University of Cantabria, 1st and 2nd Teaching Innovation Programs 2011-2012, 2013-2014, Projects Innodesign 1 and 2

In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security Problems

The remarkable success of the use of machine learning-based solutions for network security problems has been impeded by the developed ML models' inability to maintain efficacy when used in different network environments exhibiting different network behaviors. This issue is commonly referred to as the generalizability problem of ML models. The community has recognized the critical role that training datasets play in this context and has developed various techniques to improve dataset curation to overcome this problem. Unfortunately, these methods are generally ill-suited or even counterproductive in the network security domain, where they often result in unrealistic or poor-quality datasets. To address this issue, we propose an augmented ML pipeline that leverages explainable ML tools to guide the network data collection in an iterative fashion. To ensure the data's realism and quality, we require that the new datasets should be endogenously collected in this iterative process, thus advocating for a gradual removal of data-related problems to improve model generalizability. To realize this capability, we develop a data-collection platform, netUnicorn, that takes inspiration from the classic "hourglass" model and is implemented as its "thin waist" to simplify data collection for different learning problems from diverse network environments. The proposed system decouples data-collection intents from the deployment mechanisms and disaggregates these high-level intents into smaller reusable, self-contained tasks. We demonstrate how netUnicorn simplifies collecting data for different learning problems from multiple network environments and how the proposed iterative data collection improves a model's generalizability

FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network with a Spatiotemporal FPGA Accelerator

Spiking Neural Networks (SNNs) are expected to be a promising alternative to Artificial Neural Networks (ANNs) due to their strong biological interpretability and high energy efficiency. Specialized SNN hardware offers clear advantages over general-purpose devices in terms of power and performance. However, there's still room to advance hardware support for state-of-the-art (SOTA) SNN algorithms and improve computation and memory efficiency. As a further step in supporting high-performance SNNs on specialized hardware, we introduce FireFly v2, an FPGA SNN accelerator that can address the issue of non-spike operation in current SOTA SNN algorithms, which presents an obstacle in the end-to-end deployment onto existing SNN hardware. To more effectively align with the SNN characteristics, we design a spatiotemporal dataflow that allows four dimensions of parallelism and eliminates the need for membrane potential storage, enabling on-the-fly spike processing and spike generation. To further improve hardware acceleration performance, we develop a high-performance spike computing engine as a backend based on a systolic array operating at 500-600MHz. To the best of our knowledge, FireFly v2 achieves the highest clock frequency among all FPGA-based implementations. Furthermore, it stands as the first SNN accelerator capable of supporting non-spike operations, which are commonly used in advanced SNN algorithms. FireFly v2 has doubled the throughput and DSP efficiency when compared to our previous version of FireFly and it exhibits 1.33 times the DSP efficiency and 1.42 times the power efficiency compared to the current most advanced FPGA accelerators

Developing Guidelines for Two-Dimensional Model Review and Acceptance

Two independent modelers ran two hydraulic models, SRH-2D and HEC-RAS 2D. The models were applied to the Lakina River (MP 44 McCarthy Road) and to Quartz Creek (MP 0.7 Quartz Creek Road), which approximately represent straight and bend flow conditions, respectively. We compared the results, including water depth, depth averaged velocity, and bed shear stress, from the two models for both modelers. We found that the extent and density of survey data were insufficient for Quartz Creek. Neither model was calibrated due to the lack of basic field data (i.e., discharge, water surface elevation, and sediment characteristics). Consequently, we were unable to draw any conclusion about the accuracy of the models. Concerning the time step and the equations used (simplified or full) to solve the momentum equation in the HEC-RAS 2D model, we found that the minimum time step allowed by the model must be used if the diffusion wave equation is used in the simulations. A greater time step can be used if the full momentum equation is used in the simulations. We developed a set of guidelines for reviewing model results, and developed and provided a two-day training workshop on the two models for ADOT&PF hydraulic engineers

FAST ROTATED BOUNDING BOX ANNOTATIONS FOR OBJECT DETECTION

Traditionally, object detection models use a large amount of annotated data and axis-aligned bounding boxes (AABBs) are often chosen as the image annotation technique for both training and predictions. The purpose of annotating the objects in the images is to indicate the regions of interest with the corresponding labels. Accurate object annotations help the computer vision models to understand the distinct patterns of the image features to recognize and localize different classes of objects. However, AABBs are often a poor fit for elongated object instances. It’s also challenging to localize objects with AABBs in densely packed aerial images because of overlapping adjacent bounding boxes. Alternatively, using rectangular annotations that can be oriented diagonally, also known as rotated bounding boxes (RBB), can provide a much tighter fit for elongated objects and reduce the potential bounding box overlap between adjacent objects. However, RBBs are much more time-consuming and tedious to annotate than AABBs for large datasets. In this work, we propose a novel annotation tool named as FastRoLabelImg (Fast Rotated LabelImg) for producing high-quality RBB annotations with low time and effort. The tool generates accurate RBB proposals for objects of interest as the annotator makes progress through the dataset. It can also adapt available AABBs to generate RBB proposals. Furthermore, a multipoint box drawing system is provided to reduce manual RBB annotation time compared to the existing methods. Across three diverse datasets, we show that the proposal generation methods can achieve a maximum of 88.9% manual workload reduction. We also show that our proposed manual annotation method is twice as fast as the existing system with the same accuracy by conducting a participant study. Lastly, we publish the RBB annotations for two public datasets in order to motivate future research that will contribute in developing more competent object detection algorithms capable of RBB predictions