Multi-factor optimization of bio-methanol production through gasification process via statistical methodology coupled with genetic algorithm

This work innovatively explores the bio-methanol production process, conducts comprehensive analyses, develops statistical models, and optimizes operational conditions, contributing valuable insights to the field of sustainable energy production from biomass. Accordingly, bio-methanol production from biomass through gasification route was investigated and simulated using Aspen Plus software. The effects of operational parameters on energy duty of gasification reactor and the methanol production rate in syngas to methanol reactor were investigated. The parameters affecting the process performance including temperature, pressure, and steam/feed ratio were examined using the response surface methodology (RSM) by central composite design (CCD) technique. Analysis of variance (ANOVA) was performed, and two quadratic models were derived. The predicted R2 values of these models for methanol mass flowrate and energy duty were 0.9394 and 0.9363, respectively. The optimal operational conditions were identified using genetic algorithm (GA). The optimum values of temperature, pressure, and steam/feed ratio in gasification reactor were 900 ◦C, 4 bar, and 0.675, respectively. This condition leads to methanol mass flowrate and energy duty of 4.254 kg/s and 40736.355 kw, respectively. In addition, sensitivity analysis was performed on syngas to methanol reactor performance

The Prevalence of Hepatitis C Virus (HCV) Infection and Genotypes in Patients with Hemophilia and Other Blood Coagulopathies in Mashhad, Iran

Background and Aim: Patients with blood coagulopathies treated with multiple transfusions have a high risk to acquire some viral infections such as hepatitis C. This research was aimed to identify hepatitis C virus (HCV) infection prevalence, and the viral genotypes among patients with hemophilia and other inherited coagulopathies in Mashhad, Iran. Methods: Medical records of 760 patients with inherited coagulopathies including hemophilia were reviewed in Sarvar Clinic of Mashhad. Plasma samples were subjected to detect antibodies against HCV (anti-HCV) by enzyme-linked immunosorbent assay. HCV RNA and genotypes were determined by a real-time polymerase chain reaction (PCR) method. Results: Totally 128 individuals (16.8%) including patients with hemophilia (n=116) and individuals with other coagulopathies (n=12) were found to be seropositive for anti-HCV. They comprised 122 men and six women with a mean age of 31.6 ± 10.5 years. The PCR results were available for 118 patients, of whom 86 individuals (72.9%) were found to have detectable HCV RNA in their serum. The most frequent genotypes were 1a and 3a (49.1% and 35.8%, respectively). HCV genotypes were not significantly correlated with the patients’ age (p=0.477) as well as with the serum levels of alanine aminotransferase (p=0.655) and aspartate aminotransferase (p=0.332). Conclusion: The patients with blood coagulation disorders had a greater prevalence of HCV infection in comparison with the general population in our region. The most common subgenotypes of HCV were 1a, and 3a, respectively. These results could assist professionals to choose more efficient approaches for the management of their patients. *Corresponding Author: Mohammad Reza Hedayati-Moghaddam; Email: [email protected] Please cite this article as: Badiei Z, Ahmadi-Ghezeldasht S, Sima HR, Habibi M, Khamooshi M, Azimi A, Hedayati-Moghaddam MR. The Prevalence of Hepatitis C Virus (HCV) Infection and Genotypes in Patients with Hemophilia and Other Blood Coagulopathies in Mashhad, Iran. Arch Med Lab Sci. 2021;7:1-7 (e9). https://doi.org/10.22037/amls.v7.3396

Stock market index prediction using artificial neural network

In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been assessed. The methodology used in this study considered the short-term historical stock prices as well as the day of week as inputs. Daily stock exchange rates of NASDAQ from January 28 2015 to 18 June 2015 are used to develop a robust model. First 70 days (January 28 to March 7) are selected as training dataset and the last 29 days are used for testing the model prediction ability. Networks for NASDAQ index prediction for two type of input dataset (four prior days and nine prior days) were developed and validated.En este estudio se investigó la capacidad de previsión del índice bursátil diario NASDAQ por parte de la red neuronal artificial (RNA). Se evaluaron diversas RNA proalimentadas que fueron entrenadas mediante un algoritmo de retropropagación. La metodología utilizada en este estudio consideró como inputs los precios bursátiles históricos a corto plazo así como el día de la semana. Se utilizaron los índices bursátiles diarios de NASDAQ del 28 de enero al 18 de junio de 2015 para desarrollar un modelo robusto. Se seleccionaron los primeros 70 días (del 28 de enero al 7 de marzo) como conjuntos de datos de entrenamiento y los últimos 29 días para probar la capacidad del modelo de predicción. Se desarrollaron y validaron redes para la predicción del índice NASDAQ para dos tipos de conjuntos de datos de input (los cuatro y los nueve días previos)

Stock market index prediction using artificial neural network

AbstractIn this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been assessed. The methodology used in this study considered the short-term historical stock prices as well as the day of week as inputs. Daily stock exchange rates of NASDAQ from January 28, 2015 to 18 June, 2015 are used to develop a robust model. First 70 days (January 28 to March 7) are selected as training dataset and the last 29 days are used for testing the model prediction ability. Networks for NASDAQ index prediction for two type of input dataset (four prior days and nine prior days) were developed and validated

Human T-Lymphotropic Virus Type I (HTLV-1) Infection among Iranian Blood Donors: First Case-Control Study on the Risk Factors

Human T-cell lymphotropic virus type 1 (HTLV-1) infection is an endemic condition in Northeast Iran and, as such, identification of risk factors associated with the infection in this region seems to be a necessity. All the possible risk factors for HTLV-1 seropositivity among first-time blood donors were evaluated in Mashhad, Iran, during the period of 2011–2012. Blood donation volunteers were interviewed for demographic data, medical history, and behavioral characteristics and the frequencies of risk factors were compared between HTLV-1 positive (case) and HTLV-1 negative (control) donors. The data was analyzed using Chi square and t-tests. Logistic regression analysis was performed to identify independent risk factors for the infection. Assessments were carried out on 246 cases aged 17–60 and 776 controls aged 17–59, who were matched based on their ages, gender, and date and center of donation. Logistic analysis showed low income (OR = 1.53, p = 0.035), low educational level (OR = 1.64, p = 0.049), being born in the cities of either Mashhad (OR = 2.47, p = 0.001) or Neyshabour (OR = 4.30, p < 0001), and a history of blood transfusion (OR = 3.17, p = 0.007) or non-IV drug abuse (OR = 3.77, p < 0.0001) were significant predictors for infection with HTLV-1. Lack of variability or small sample size could be reasons of failure to detect some well-known risk factors for HTLV-1 infection, such as prolonged breastfeeding and sexual promiscuity. Pre-donation screening of possible risk factors for transfusion-transmissible infections should also be considered as an important issue, however, a revision of the screening criteria such as a history of transfusion for more than one year prior to donation is strongly recommended

Multi-factor optimization of bio-methanol production through gasification process via statistical methodology coupled with genetic algorithm

This work innovatively explores the bio-methanol production process, conducts comprehensive analyses, develops statistical models, and optimizes operational conditions, contributing valuable insights to the field of sustainable energy production from biomass. Accordingly, bio-methanol production from biomass through gasification route was investigated and simulated using Aspen Plus software. The effects of operational parameters on energy duty of gasification reactor and the methanol production rate in syngas to methanol reactor were investigated. The parameters affecting the process performance including temperature, pressure, and steam/feed ratio were examined using the response surface methodology (RSM) by central composite design (CCD) technique. Analysis of variance (ANOVA) was performed, and two quadratic models were derived. The predicted R2 values of these models for methanol mass flowrate and energy duty were 0.9394 and 0.9363, respectively. The optimal operational conditions were identified using genetic algorithm (GA). The optimum values of temperature, pressure, and steam/feed ratio in gasification reactor were 900 ◦C, 4 bar, and 0.675, respectively. This condition leads to methanol mass flowrate and energy duty of 4.254 kg/s and 40736.355 kw, respectively. In addition, sensitivity analysis was performed on syngas to methanol reactor performance

Multi-factor optimization of bio-methanol production through gasification process via statistical methodology coupled with genetic algorithm

This work innovatively explores the bio-methanol production process, conducts comprehensive analyses, develops statistical models, and optimizes operational conditions, contributing valuable insights to the field of sustainable energy production from biomass. Accordingly, bio-methanol production from biomass through gasification route was investigated and simulated using Aspen Plus software. The effects of operational parameters on energy duty of gasification reactor and the methanol production rate in syngas to methanol reactor were investigated. The parameters affecting the process performance including temperature, pressure, and steam/feed ratio were examined using the response surface methodology (RSM) by central composite design (CCD) technique. Analysis of variance (ANOVA) was performed, and two quadratic models were derived. The predicted R2 values of these models for methanol mass flowrate and energy duty were 0.9394 and 0.9363, respectively. The optimal operational conditions were identified using genetic algorithm (GA). The optimum values of temperature, pressure, and steam/feed ratio in gasification reactor were 900 °C, 4 bar, and 0.675, respectively. This condition leads to methanol mass flowrate and energy duty of 4.254 kg/s and 40736.355 kw, respectively. In addition, sensitivity analysis was performed on syngas to methanol reactor performance