Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications

Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Diagnóstico automático de melanoma mediante técnicas modernas de aprendizaje automático

The incidence and mortality rates of skin cancer remain a huge concern in many countries. According to the latest statistics about melanoma skin cancer, only in the Unites States, 7,650 deaths are expected in 2022, which represents 800 and 470 more deaths than 2020 and 2021, respectively. In 2022, melanoma is ranked as the fifth cause of new cases of cancer, with a total of 99,780 people. This illness is mainly diagnosed with a visual inspection of the skin, then, if doubts remain, a dermoscopic analysis is performed. The development of e_ective non-invasive diagnostic tools for the early stages of the illness should increase quality of life, and decrease the required economic resources. The early diagnosis of skin lesions remains a tough task even for expert dermatologists because of the complexity, variability, dubiousness of the symptoms, and similarities between the different categories among skin lesions. To achieve this goal, previous works have shown that early diagnosis from skin images can benefit greatly from using computational methods. Several studies have applied handcrafted-based methods on high quality dermoscopic and histological images, and on top of that, machine learning techniques, such as the k-nearest neighbors approach, support vector machines and random forest. However, one must bear in mind that although the previous extraction of handcrafted features incorporates an important knowledge base into the analysis, the quality of the extracted descriptors relies heavily on the contribution of experts. Lesion segmentation is also performed manually. The above procedures have a common issue: they are time-consuming manual processes prone to errors. Furthermore, an explicit definition of an intuitive and interpretable feature is hardly achievable, since it depends on pixel intensity space and, therefore, they are not invariant regarding the differences in the input images. On the other hand, the use of mobile devices has sharply increased, which offers an almost unlimited source of data. In the past few years, more and more attention has been paid to designing deep learning models for diagnosing melanoma, more specifically Convolutional Neural Networks. This type of model is able to extract and learn high-level features from raw images and/or other data without the intervention of experts. Several studies showed that deep learning models can overcome handcrafted-based methods, and even match the predictive performance of dermatologists. The International Skin Imaging Collaboration encourages the development of methods for digital skin imaging. Every year since 2016 to 2019, a challenge and a conference have been organized, in which more than 185 teams have participated. However, convolutional models present several issues for skin diagnosis. These models can fit on a wide diversity of non-linear data points, being prone to overfitting on datasets with small numbers of training examples per class and, therefore, attaining a poor generalization capacity. On the other hand, this type of model is sensitive to some characteristics in data, such as large inter-class similarities and intra-class variances, variations in viewpoints, changes in lighting conditions, occlusions, and background clutter, which can be mostly found in non-dermoscopic images. These issues represent challenges for the application of automatic diagnosis techniques in the early phases of the illness. As a consequence of the above, the aim of this Ph.D. thesis is to make significant contributions to the automatic diagnosis of melanoma. The proposals aim to avoid overfitting and improve the generalization capacity of deep models, as well as to achieve a more stable learning and better convergence. Bear in mind that research into deep learning commonly requires an overwhelming processing power in order to train complex architectures. For example, when developing NASNet architecture, researchers used 500 x NVidia P100s - each graphic unit cost from $5,899 to$7,374, which represents a total of $2,949,500.00 -$3,687,000.00. Unfortunately, the majority of research groups do not have access to such resources, including ours. In this Ph.D. thesis, the use of several techniques has been explored. First, an extensive experimental study was carried out, which included state-of-the-art models and methods to further increase the performance. Well-known techniques were applied, such as data augmentation and transfer learning. Data augmentation is performed in order to balance out the number of instances per category and act as a regularizer in preventing overfitting in neural networks. On the other hand, transfer learning uses weights of a pre-trained model from another task, as the initial condition for the learning of the target network. Results demonstrate that the automatic diagnosis of melanoma is a complex task. However, different techniques are able to mitigate such issues in some degree. Finally, suggestions are given about how to train convolutional models for melanoma diagnosis and future interesting research lines were presented. Next, the discovery of ensemble-based architectures is tackled by using genetic algorithms. The proposal is able to stabilize the training process. This is made possible by finding sub-optimal combinations of abstract features from the ensemble, which are used to train a convolutional block. Then, several predictive blocks are trained at the same time, and the final diagnosis is achieved by combining all individual predictions. We empirically investigate the benefits of the proposal, which shows better convergence, mitigates the overfitting of the model, and improves the generalization performance. On top of that, the proposed model is available online and can be consulted by experts. The next proposal is focused on designing an advanced architecture capable of fusing classical convolutional blocks and a novel model known as Dynamic Routing Between Capsules. This approach addresses the limitations of convolutional blocks by using a set of neurons instead of an individual neuron in order to represent objects. An implicit description of the objects is learned by each capsule, such as position, size, texture, deformation, and orientation. In addition, a hyper-tuning of the main parameters is carried out in order to ensure e_ective learning under limited training data. An extensive experimental study was conducted where the fusion of both methods outperformed six state-of-the-art models. On the other hand, a robust method for melanoma diagnosis, which is inspired on residual connections and Generative Adversarial Networks, is proposed. The architecture is able to produce plausible photorealistic synthetic 512 x 512 skin images, even with small dermoscopic and non-dermoscopic skin image datasets as problema domains. In this manner, the lack of data, the imbalance problems, and the overfitting issues are tackled. Finally, several convolutional modes are extensively trained and evaluated by using the synthetic images, illustrating its effectiveness in the diagnosis of melanoma. In addition, a framework, which is inspired on Active Learning, is proposed. The batch-based query strategy setting proposed in this work enables a more faster training process by learning about the complexity of the data. Such complexities allow us to adjust the training process after each epoch, which leads the model to achieve better performance in a lower number of iterations compared to random mini-batch sampling. Then, the training method is assessed by analyzing both the informativeness value of each image and the predictive performance of the models. An extensive experimental study is conducted, where models trained with the proposal attain significantly better results than the baseline models. The findings suggest that there is still space for improvement in the diagnosis of skin lesions. Structured laboratory data, unstructured narrative data, and in some cases, audio or observational data, are given by radiologists as key points during the interpretation of the prediction. This is particularly true in the diagnosis of melanoma, where substantial clinical context is often essential. For example, symptoms like itches and several shots of a skin lesion during a period of time proving that the lesion is growing, are very likely to suggest cancer. The use of different types of input data could help to improve the performance of medical predictive models. In this regard, a _rst evolutionary algorithm aimed at exploring multimodal multiclass data has been proposed, which surpassed a single-input model. Furthermore, the predictive features extracted by primary capsules could be used to train other models, such as Support Vector Machine

Inverse Problems in High Dimensional Stochastic Systems Under Uncertainty.

Increasingly often, problems in modern medicine, quantitative finance, or social-networking involve tens of thousands of variables that interact with each other and jointly evolve over time. The states of these variables may correspond to the phenotype of a particular individual, the price of a security, or the current status of an individual's social networking profile. If these states are hidden to a researcher, additional information must be obtained to infer these hidden states based upon measurements of other variables, knowledge of the interacting network structure, and any dynamics that model the evolution of these states. This dissertation is an attempt to address general problems regarding reasoning under uncertainty in such spatio-temporal models but with an emphasis to applications in predictive health and disease in a loosely monitored population of individuals. The motivation is highly interdisciplinary and draws on tools and concepts from machine learning, statistics, epidemiology, bioinformatics, and physics. We begin by presenting a solution to recursively sampling the best subset of nodes/variables that elicit the largest expected information gain of all sampled and un-sampled nodes in a large spatio-temporal complex network. We then present a tractable method for empirically estimating the spatio-temporal graphical model structure corresponding to the "susceptible", "infected", and "recovered" (SIR) model of mathematical epidemiology. Here, we formulate the problem as an L1-penalized likelihood convex program and produce network detection performance superior to other comparable state of the art methods. We present a logistic regression classifier that is robust to worst-case bounded measurement uncertainty. The proposed method produces superior worst-case detection performance to the standard L1-logistic regression classifier on a Human rhinovirus (HRV) gene expression data set. The final chapter concludes with identifying the appropriate basis functions used in a classification model when the data is both high-dimensional and temporally sampled with ultimate goal of discriminating between multiple states/labels, e.g., phenotypes. We utilize Gaussian Processes and L1-logistic regression to accomplish this task and apply it to a human gene expression time-series data set resulting from a challenge study inoculation with Human Influenza A/H3N2, HRV, and Human respiratory syncytial virus (RSV).Ph.D.BioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78831/1/plhjr_1.pd

A Framework for Augmenting Building Performance Models Using Machine Learning and Immersive Virtual Environment

Building performance models (BPMs), such as building energy simulation models, have been widely used in building design. Existing BPMs are mainly derived using data from existing buildings. They may not be able to effectively address human-building interactions and lack the capability to address specific contextual factors in buildings under design. The lack of such capability often contributes to the existence of building performance discrepancies, i.e., differences between predicted performance during design and the actual performance. To improve the prediction accuracy of existing BPMs, a computational framework is developed in this dissertation. It combines an existing BPM with context-aware design-specific data involving human-building interactions in new designs by using a machine learning approach. Immersive virtual environments (IVEs) are used to acquire data describing design-specific human-building interactions, a machine learning technique is used to combine data obtained from an existing BPM, and IVEs are used to generate an augmented BPM. The potential of the framework is investigated and evaluated. An artificial neural network (ANN)-based greedy algorithm combines context-aware design-specific data obtained from IVEs with an existing BPM to enhance the simulations of human-building interactions in new designs. The results of the application show the potential of the framework to improve the prediction accuracy of an existing BPM evaluated against data obtained from the physical environment. However, it lacks the ability to determine the appropriate combination between context-aware design-specific data and data of the existing BPM. Consequently, the framework is improved to have ability to determine an appropriate combination based on a specified performance target. A generative adversarial network (GAN) is used to combine context-aware design-specific data and data of an existing BPM using the performance target as guide to generate an augmented BPM. The results confirm the effectiveness of this new framework. The performance of the augmented BPMs generated using the GAN-based framework is significantly better than the updated BPMs generated using the ANN-based greedy algorithm. The framework is completed by incorporating a robustness analysis to assist investigations of robustness of the GAN regarding the uncertainty involved in the input parameters (i.e., an existing BPM and context-aware design-specific data). Overall, this dissertation shows the promising potential of the framework in enhancing performance of BPMs and reducing performance discrepancies between estimations made during design and in performance in actual buildings

Fuzzy expert systems in civil engineering

Imperial Users onl

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man