Optimization methods in inverse problems and applications to science and engineering

Optimization and Engineering -     

Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.     

A practical assessment of risk-averse approaches in production lot-sizing problems

ABSTRACT

This paper presents an empirical assessment of four state-of-the-art risk-averse approaches to deal with the capacitated lot-sizing problem under stochastic demand. We analyse two mean-risk models based on the semideviation and on the conditional value-at-risk risk measures, and alternate first and second-order stochastic dominance approaches. The extensive computational experiments based on different instances characteristics and on a case-study suggest that CVaR exhibits a good trade-off between risk and performance, followed by the semideviation and first-order stochastic dominance approach. For all approaches, enforcing risk-aversion helps to reduce the cost-standard deviation substantially, which is usually accomplished via increasing production rates. Overall, we can say that very risk-averse decision-makers would be willing to pay an increased price to have a much less risky solution given by CVaR. In less risk-averse settings, though, semideviation and first-order stochastic dominance can be appealing alternatives to provide significantly more stable production planning costs with a marginal increase of the expected costs.     

Polyetherimide-LaNi5 composite films for hydrogen storage applications

This study investigates the preparation of polyetherimide (PEI) – LaNi₅ composites films for hydrogen storage. Prior to the polymer addition, LaNi₅ was ball-milled at different conditions (250, 350, and 450 RPM) and annealed at 500 °C for 1 h under vacuum. The composites were produced with BM-LaNi₅-350 (PEI/LaNi₅-350) and annealed BM-LaNi₅-350 (PEI/LaNi₅-350-TT). Membranes were successfully produced through solvent casting assisted by an ultrasonic bath. The particles dispersion and the film morphology did not change after hydrogenation cycles. In the H₂ sorption experiments at 43 °C and 20 bar, the films stored H₂ without incubation time; both samples reached a capacity of ~0.6 wt%. The H₂ sorption kinetics of PEI/LaNi₅-350 was comparable to that of BM-LaNi₅-350, whereas PEI/LaNi₅-350-TT presented significantly slower kinetics. LaNi₅ oxidation was hindered by PEI, showing that it can be explored to improve metal hydrides air resistance. The results demonstrated that PEI films filled with LaNi₅ are promising materials for hydrogen storage.     

Visceral Obesity and Its Shared Role in Cancer and Cardiovascular Disease: A Scoping Review of the Pathophysiology and Pharmacological Treatments

The association between obesity, cancer and cardiovascular disease (CVD) has been demonstrated in animal and epidemiological studies. However, the specific role of visceral obesity on cancer and CVD remains unclear. Visceral adipose tissue (VAT) is a complex and metabolically active tissue, that can produce different adipokines and hormones, responsible for endocrine-metabolic comorbidities. This review explores the potential mechanisms related to VAT that may also be involved in cancer and CVD. In addition, we discuss the shared pharmacological treatments which may reduce the risk of both diseases. This review highlights that chronic inflammation, molecular aspects, metabolic syndrome, secretion of hormones and adiponectin associated to VAT may have synergistic effects and should be further studied in relation to cancer and CVD. Reductions in abdominal and visceral adiposity improve insulin sensitivity, lipid profile and cytokines, which consequently reduce the risk of CVD and some cancers. Several medications have shown to reduce visceral and/or subcutaneous fat. Further research is needed to investigate the pathophysiological mechanisms by which visceral obesity may cause both cancer and CVD. The role of visceral fat in cancer and CVD is an important area to advance. Public health policies to increase public awareness about VAT’s role and ways to manage or prevent it are needed.     

Real-time organic aerosol chemical speciation in the indoor environment using extractive electrospray ionization mass spectrometry

Understanding the sources and composition of organic aerosol (OA) in indoor environments requires rapid measurements, since many emissions and processes have short timescales. However, real-time molecular-level OA measurements have not been reported indoors. Here, we present quantitative measurements, at a time resolution of five seconds, of molecular ions corresponding to diverse aerosol-phase species, by applying extractive electrospray ionization mass spectrometry (EESI-MS) to indoor air analysis for the first time, as part of the highly instrumented HOMEChem field study. We demonstrate how the complex spectra of EESI-MS are screened in order to extract chemical information and investigate the possibility of interference from gas-phase semivolatile species. During experiments that simulated the Thanksgiving US holiday meal preparation, EESI-MS quantified multiple species, including fatty acids, carbohydrates, siloxanes, and phthalates. Intercomparisons with Aerosol Mass Spectrometer (AMS) and Scanning Mobility Particle Sizer suggest that EESI-MS quantified a large fraction of OA. Comparisons with FIGAERO-CIMS shows similar signal levels and good correlation, with a range of 100 for the relative sensitivities. Comparisons with SV-TAG for phthalates and with SV-TAG and AMS for total siloxanes also show strong correlation. EESI-MS observations can be used with gas-phase measurements to identify co-emitted gas- and aerosol-phase species, and this is demonstrated using complementary gas-phase PTR-MS observations.     

Mining statistically significant associations for exploratory analysis of human sleep data.

We introduce a specialized association rule mining technique that can extract patterns from complex sleep data comprising polysomnographic recordings, clinical summaries, and sleep questionnaire responses. The rules mined can describe associations among temporally annotated events and questionnaire or summary data; e.g., the likelihood that an occurrence of a rapid eye movement (REM) sleep stage during the second 100 sleep epochs of the night is associated with moderate caffeine intake. We use chi2 analysis to ensure statistical significance of the mined rules at the level P < 0.05. Our results, obtained by mining sleep-related data from 242 human subjects, reveal clinically interesting associations among the polysomnographic and summary variables. Our experience suggests that association mining may also be useful for selection of variables prior to using logistic regression.     

An integrated system to remote monitor and control anaerobic wastewater treatment plants through the internet.

The TELEMAC project brings new methodologies from the Information and Science Technologies field to the world of water treatment. TELEMAC offers an advanced remote management system which adapts to most of the anaerobic wastewater treatment plants that do not benefit from a local expert in wastewater treatment. The TELEMAC system takes advantage of new sensors to better monitor the process dynamics and to run automatic controllers that stabilise the treatment plant, meet the depollution requirements and provide a biogas quality suitable for cogeneration. If the automatic system detects a failure which cannot be solved automatically or locally by a technician, then an expert from the TELEMAC Control Centre is contacted via the internet and manages the problem.