Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels. Artificial neural networks with Bayesian regularization are more robust than traditional back-propagation networks and can reduce or eliminate the need for tedious cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of ridge regression. The main objective of this work was to develop an artificial neural network to predict silicon content in hot metal by varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, and 100 neurons. The results show that all neural networks converged and presented reliable results, neural networks with 20, 25, and 30 neurons showed the best overall results. However, In short, Bayesian neural networks can be used in practice because the actual values correlate excellently with the values calculated by the neural network

Forecast of Carbon Consumption of a Blast Furnace Using Extreme Learning Machine and Probabilistic Reasoning

Blast furnaces are chemical metallurgical reactors for the production of pig iron and slag. The raw materials used (metallic feedstock) are sinter, granulated ore and pellets. The main fuel is metallurgical coke. Considering the existing difficulties in the field of simulation of complex processes, the application of solutions based on neural networks has gained space due to its diversity of application and increase in the reliability of responses. The Extreme Learning Machine is a way to train an artificial neural network (ANN) with only one hidden layer. The database used for numerical simulation corresponds to 3.5 years of reactor operation. Big Data contains 94875 pieces of information divided into 75 variables. The input of the ELM neural network is composed of 72 variables and the output of 3 variables. The selected output variables were coke rate, PCI rate and fuel rate. Artificial neural networks using extreme learning machines and using Big Data are able to predict fuel consumption based on the parameters of the reduction process in blast furnaces, and this can be verified by the accuracy of the model

kinetics study of co2 absorption in potassium carbonate solution promoted by diethylenetriamine

Abstract In this work, characterization and kinetics of CO2 absorption in potassium carbonate (K2CO3) solution promoted by diethylenetriamine (DETA) were investigated. Kinetics measurements were performed using a stirred cell reactor in the temperature range of 303.15-323.15 K and total concentration up to 2.5 kmol/m3. The density, viscosity, physical solubility, CO2 diffusivity and absorption rate of CO2 in the solution were determined. The reaction kinetics between CO2 and K2CO3 + DETA solution were examined. Pseudo-first order kinetic constants were also predicted by zwitterion mechanism. It was revealed that the addition of small amounts of DETA to K2CO3 results in a significant enhancement in CO2 absorption rate. The reaction order and activation energy were found to be 1.6 and 35.6 kJ/mol, respectively. In terms of reaction rate constant, DETA showed a better performance compared to the other promoters such as MEA, EAE, proline, arginine, taurine, histidine and alanine

Enhancement of CO2 removal by promoted MDEA solution in a hollow fiber membrane contactor: A numerical and experimental study

In this work, carbon dioxide (CO2) loading capacity of methyldiethanolamine (MDEA) solution promoted by potassium lysinate (KLys) was experimentally measured by using a gas absorption setup at different concentrations and temperatures. The CO2 removal efficiency of the MDEA + KLys solution was investigated for a CO2/N2 gas mixture by using computational fluid dynamic (CFD) simulations in a hollow fiber membrane contactor (HFMC). The effects of operating conditions including solvent concentration, solvent flow rate, gas flow rate, inlet CO2 concentration and module length on the CO2 removal efficiency were also studied. The experimental results revealed that CO2 loading capacity increases with increasing KLys concentration in the solution, while decreases as temperature increases. The simulation results indicated that MDEA + KLys solution has higher CO2 removal efficiency compared to pristine MDEA and MEA solutions. The CO2 removal efficiency increases with increasing solvent concentration, solvent flow rate and module length, whereas decreases as gas flow rate increases. The zeolitic imidazolate framework-8 (ZIF-8), as sorbent, was then incorporated into the MDEA + KLys solution and its effect on the CO2 removal efficiency was also examined. The MDEA + KLys + ZIF-8 nano-absorbent showed higher CO2 removal efficiency than that of MDEA + KLys absorbent, where introducing 0.4 wt.% ZIF-8 enhanced CO2 removal from ⁓96% to ⁓99%. The results of this work suggest that both MDEA + KLys absorbent and MDEA + KLys + ZIF-8 nano-absorbent are promising candidates for CO2 absorption processes. However, for practical use as well as a complete investigation, their behavior should be assessed by using other parameters of solvent such as reactivity with CO2, corrosion rate, and regeneration performance

A novel sensor measuring local voidage profile inside a fluidised bed reactor

Liquid-solid fluidisation is frequently encountered in drinking water treatment processes, often to obtain a large liquid-solid interfacial surface area. A large surface area is crucial for optimal seeded crystallisation in full-scale softening reactors. Due to crystallisation, particles grow and migrate to a lower zone in the reactor which leads to a stratified bed. Larger particles adversely affect the surface area. To maintain optimal process conditions in the fluidised beds, information is needed about the distribution of particle size, local voidage and available surface area, over the reactor height. In this work, a sensor is developed to obtain the hydraulic state gradient, based on Archimedes’ principle. A cylindrical heavy object is submerged in the fluidised bed and lowered gradually while its weight is measured at various heights using a sensitive force measuring device. Based on accurate fluidisation experiments with calcite grains, the voidage is determined and a straightforward empirical model is developed to estimate the particle size as a function of superficial fluid velocity, kinematic viscosity, suspension density, voidage and particle density. The surface area and specific space velocity can be estimated accordingly, which represent key performance indicators regarding the hydraulic state of the fluidised bed reactor. The prediction error for voidage is 5 ± 2 % and for particle size 9 ± 4 %. The newly developed soft sensor is a more time-effective method for obtaining the hydraulic state in full-scale liquid-solid fluidised bed reactors

Evaluation of the use of blast furnace slag as an additive in mortars

Abstract Clinker is the raw material used in the manufacture of cement. However, this material is very harmful to the environment, since it is estimated that for every ton of clinker produced, about 1.0 ton of CO2 is released into the atmosphere. For this reason, alternatives were sought for the use of other materials that are less harmful to the environment. This has led to the use of industrial by-products with the aim of increasing their use and thus reducing the amount of carbon released into the atmosphere. Blast furnace slag is a by-product used in the manufacture of some cementitious products. The aim of this research is to conduct a study on the use of slag as an additive for cement or concrete. The mortar samples were tested according to Brazilian, American and European technical standards. Physical, chemical and compressive strength tests were carried out which confirmed the possibility of using the slag without chemical or thermal activation

AVALIAÇÃO DA INCORPORAÇÃO DE RESÍDUO DE CORTE DE MÁRMORE E GRANITO EM CONCRETO PARA PRODUÇÃO DE PISOS INTERTRAVADOS PARA PAVIMENTAÇÃO

A indústria da construção civil é uma atividade que consome elevado volume de recursos naturais e no cenário atual é imprescindível se preocupar com o desenvolvimento sustentável e encontrar alternativas de reaproveitamento de resíduos sólidos. Nesse contexto, a reinserção do resíduo de corte de mármore e granito (RCMG) na cadeia produtiva é uma alternativa para amenizar um sério problema ambiental, pois o consumo de rochas ornamentais vem crescendo e elevando a quantidade de resíduo produzido. Devido ao grande volume de resíduos de corte de granito produzido e não reutilizado, este artigo técnico avalia a viabilidade técnica da sua utilização como adição em concretos e produção de blocos intertravados com resistência à compressão mínima de 35 MPa e a absorção d'água de 6% no máximo. As matérias primas foram caracterizadas quanto à massa específica, massa unitária, distribuição do tamanho de partículas, materiais pulverulentos e composição química (FRX). Em seguida, os corpos de prova foram moldados de acordo com as prescrições da norma técnica NBR 9781:2013 e foram realizadas análises de desempenho mecânico (resistência à compressão) e absorção d'água. Em suma, a partir dos resultados obtidos no programa experimental, o uso do resíduo de corte de mármore e granito como adição em concretos para produção de pisos de pavimentação é viável tecnicamente para tráfego de pedestres, veículos leves e veículos comerciais de linha

Head and neck radiotherapy amid the COVID‑19 pandemic: practice recommendations of the Italian Association of Radiotherapy and Clinical Oncology (AIRO)

Abstract Management of patients with head and neck cancers (HNCs) is challenging for the Radiation Oncologist, especially in the COVID-19 era. The Italian Society of Radiotherapy and Clinical Oncology (AIRO) identified the need of practice recommendations on logistic issues, treatment delivery and healthcare personnel’s protection in a time of limited resources. A panel of 15 national experts on HNCs completed a modified Delphi process. A five-point Likert scale was used; the chosen cut-offs for strong agreement and agreement were 75% and 66%, respectively. Items were organized into two sections: (1) general recommendations (10 items) and (2) special recommendations (45 items), detailing a set of procedures to be applied to all specific phases of the Radiation Oncology workflow. The distribution of facilities across the country was as follows: 47% Northern, 33% Central and 20% Southern regions. There was agreement or strong agreement across the majority (93%) of proposed items including treatment strategies, use of personal protection devices, set-up modifications and follow-up re-scheduling. Guaranteeing treatment delivery for HNC patients is well-recognized in Radiation Oncology. Our recommendations provide a flexible tool for management both in the pandemic and post-pandemic phase of the COVID-19 outbreak

CFD Modeling of Fluidized Beds

We review the mathematical modeling of fluidized suspensions with focus on the Eulerian (or multifluid) approach. After a brief survey of different modeling approaches adopted for multiphase flows, we discuss the Eulerian equations of motion for fluidized suspensions of a finite number of monodisperse particle classes, obtained by volume averaging. We present the problem of closure for the stress tensors and the interaction forces between the phases and report some of the constitutive relations used for them in the literature. Finally, we explain briefly the population balance modeling approach, which allows handling suspensions of particles continuously distributed over any of their properties of interest

Clinical features and outcomes of elderly hospitalised patients with chronic obstructive pulmonary disease, heart failure or both

Background and objective: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) mutually increase the risk of being present in the same patient, especially if older. Whether or not this coexistence may be associated with a worse prognosis is debated. Therefore, employing data derived from the REPOSI register, we evaluated the clinical features and outcomes in a population of elderly patients admitted to internal medicine wards and having COPD, HF or COPD + HF. Methods: We measured socio-demographic and anthropometric characteristics, severity and prevalence of comorbidities, clinical and laboratory features during hospitalization, mood disorders, functional independence, drug prescriptions and discharge destination. The primary study outcome was the risk of death. Results: We considered 2,343 elderly hospitalized patients (median age 81 years), of whom 1,154 (49%) had COPD, 813 (35%) HF, and 376 (16%) COPD + HF. Patients with COPD + HF had different characteristics than those with COPD or HF, such as a higher prevalence of previous hospitalizations, comorbidities (especially chronic kidney disease), higher respiratory rate at admission and number of prescribed drugs. Patients with COPD + HF (hazard ratio HR 1.74, 95% confidence intervals CI 1.16-2.61) and patients with dementia (HR 1.75, 95% CI 1.06-2.90) had a higher risk of death at one year. The Kaplan-Meier curves showed a higher mortality risk in the group of patients with COPD + HF for all causes (p = 0.010), respiratory causes (p = 0.006), cardiovascular causes (p = 0.046) and respiratory plus cardiovascular causes (p = 0.009). Conclusion: In this real-life cohort of hospitalized elderly patients, the coexistence of COPD and HF significantly worsened prognosis at one year. This finding may help to better define the care needs of this population