Prediction of NOx Emissions with A Novel ANN Model in Adana

NOx exmissions are one of the typical air pollutants that has drawn worldwide attention. NO emissions from air cause detrimental effects on the environment and human health such as lung cancer, asthma, allergic rhinitis, and mental diseases. Therefore, real-time NOx monitoring has been very popular research topics in atmospheric and environmental science. However, the spatial coverage of monitoring stations within Adana is limited and thus often insufficient for exposure. Moreover, NOx monitoring stations are also lacking to reveal the influences of meteorological and air pollutant effects. In this study, artificial neural network (ANN), which is a biological mimicked computer algorithm that simulates the functions of neurons using artificial neurons, has been used to present a quantitative determination of the NOx emission in Adana through the influences of temperature (°C), wind rate (km/h), and SO2 (µg/m³) on NOx emissions. The high R2 values in testing dataset lead to the conclusion that the artificial neural network model provides predictions. The developed model in study is a useful tool for the design and planning of air pollution control policies as well as reducing economic cost. The developed model in study is a useful tool for the design and planning of air pollution control policies as well as reducing economic cost

Investigation of cattle manure gasification in a downdraft gasifier using aspen plus

Although the Aspen Plus process simulator is widely used to model biomass gasification processes, to our best knowledge, no study has yet been reported for the cattle manure downdraft gasification process. This study presents downdraft gasification characteristics of the cattle manure by performing sensitivity analysis in the Aspen Plus process simulator. Effects of gasifier temperature (between 600-1000 °C) and steam/biomass ratio (between 0.5-2.0) on syngas composition and syngas lower heating value (LHV) were discussed. Sensitivity analysis results indicated the higher ratios of steam/biomass and gasifier temperature of 750 °C are the optimum conditions to produce H2-rich syngas (more than 50% on dry basis). On the other hand, high gasification temperatures and low steam/biomass ratios should be preferred in order to obtain syngas with high calorific value

A comparative study for biomass gasification in bubbling bed gasifier using Aspen HYSYS

This study presents bubbling bed gasification characteristics of the agricultural and livestock wastes performing sensitivity analysis in the Aspen HYSYS process simulator. Effects of operating conditions on syngas composition, syngas exergy, and syngas lower heating value were examined. Sensitivity analysis results indicated the optimum steam/biomass ratio (0.2–0.3) and gasifier temperature (700 °C–800 °C) to produce syngas with the highest quality. The novelty of the work can be divided into two parts: initially, it is a comparative study for gasification of agricultural and livestock wastes in bubbling bed gasifier and secondly, although fluidized gasifiers have been modeled and comparative studies have been conducted with Aspen Plus® before, there are no similar analyses for bubbling bed gasifiers for agricultural and livestock wastes in Aspen HYSYS, according to our best knowledge. The deductions of this study are significant in terms of development of bubbling bed gasifiers for biomass. © 2020 Elsevier Lt

Aspen plus programı kullanılarak katı oksit yakıt hücresinin çalışma performansının incelenmesi

Katı oksit yakıt hücreleri, temiz bir enerji üretim teknoloji olması sebebiyle son yıllarda oldukça dikkat çekmektedir. Katı oksit yakıt hücreleri diğer yakıt hücreleri ile karşılaştırıldığında; yüksek verimlilikte enerji üretimi, yakıt esnekliği ve elektrik üretim kapasitesi yüksekliği avantajlarıyla tercih edilmektedir. Aspen Plus benzeşim programının sahip olduğu esneklik nedeni ile araştırmacılar tarafından yakıt hücrelerinin tasarımı ve optimizasyonu için tercih edilmektedir. Bu çalışmada, Aspen Plus benzeşim programı kullanılarak katı oksit yakıt hücresine beslenen yakıt içeriğindeki hidrojen kompozisyonunun ve yakıt hücresi sıcaklığının yakıt hücresinin verimi üzerindeki etkisi incelenmiştir

Çan Kömürü Gazlaştırılmasının Sürüklemeli Akış Gazlaştırıcıda Aspen PLUS® Kullanılarak İncelenmesi

Excessive consumption of fossil fuels due to energy demand leads to an increase in the amount of CO2emitted to the environment. Gasification technology enables clean and efficient use of fossil fuels such ascoal, which cause CO2 emissions predominantly. Gasification can be utilized under several atmospheressuch as air, steam, O2/CO2 mixture, etc. The reduction of inert gases (N2) and the increase of COconcentration at high temperatures in syngas provides high-quality gas. Among the commercial gasifiers,entrained flow gasifiers have many advantages such as obtaining tar-free synthesis gas, high carbonconversion efficiency, and production in high capacities. In addition, there is no limitation to the type ofcoal to be used. The performance of the entrained flow gasifiers can be examined by simulation programsand design optimization can be performed at a low cost. This study aims to develop a new entrained flowgasifier model for Turkish Lignite (Çan coal) using the Aspen Plus® thermodynamic simulation programand the effects of various parameters on the synthesis gas were investigated by sensitivity analysis.Enerji talebi nedeniyle aşırı fosil yakıt tüketimi, çevreye yayılan CO2 miktarında artışa neden olmaktadır._x000D_ Gazlaştırma teknolojisi, CO2 emisyonuna neden olan kömür gibi fosil yakıtların temiz ve verimli_x000D_ kullanılmasını sağlar. Hava, buhar ve O2/CO2 karışımı gazlaştırma atmosferi olarak kullanılabilir. İnert_x000D_ gazların azaltılması (N2) ve sentez gazında yüksek sıcaklıklarda CO konsantrasyonunun artması yüksek_x000D_ kaliteli gaz elde edilmesine olanak tanır. Ticari gazlaştırıcılar arasında, sürüklemeli gazlaştırıcılarının_x000D_ katransız sentez gazı elde etme, yüksek karbon dönüşüm verimliliği ve yüksek kapasitelerde üretim gibi_x000D_ birçok avantajı bulunmaktadır. Ayrıca, sürüklemeli gazlaştırıcılarda kullanılacak kömür çeşidine bağlı_x000D_ olarak herhangi bir sınırlama yoktur. Sürüklemeli gazlaştırıcılarının performansı genellikle simülasyon_x000D_ programları ile incelenirken, düşük maliyetlerle tasarım ve optimizasyon yapılabilmektedir. Bu_x000D_ çalışmanın amacı Aspen Plus termodinamik simülasyon programını kullanarak Türk Linyitleri (Çan kömürü) için yeni bir sürüklemeli akış gazlaştırıcı modeli geliştirmektir ve gazlaştırıcıya ait çalışma_x000D_ parametrelerinin sentez gazı üzerindeki etkilerini parametrik çalışma yaparak incelemektir

Prediction of chemical exergy of syngas from downdraft gasifier by means of machine learning

The rapid consumption of fossil fuels because of the increasing energy demand caused the increase in greenhouse gas emissions. However, biomass gasification is attracting much attention as an environmentally friendly and highly efficient thermochemical conversion due to its high carbon conversion and low greenhouse gas emissions. Further, downdraft gasifiers are known as the most suitable technology for biomass gasification processes because they offer an easy-to-control working environment and low investment cost. In recent years, artificial neural network models (ANN) have been used in the literature as a machine learning approach to predict gasification parameters. In this work, the parametric study was carried out for the variation of gasifier temperature (873.15 K-1173.15 K) and steam/biomass ratio (0.1-1.5) for 22 lignocellulosic biomass samples. Thus, 32,025 different experimental conditions generated by Aspen Plus (R) were used with Bayesian regularized ANN as a machine learning approach to predict the chemical exergy of the syngas from the downdraft gasifier. The operating parameters of gasifier temperature and steam/biomass ratio were found to be highly influential on the syngas quality and chemical exergy value of the syngas. Therefore, the operating conditions and biomass properties (carbon, hydrogen and oxygen content) were selected as input parameters for the ANN model. The regression coefficients (R-2) were found to be convincingly 0.9992, 0.9991 and 0.9942 for training, test and hazelnut shell gasification data, respectively. Moreover, the results for root mean squared error (RMSE) were within satisfactory limits for the developed ANN model

Artificial Intelligence Approach in Gasification Integrated Solid Oxide Fuel Cell Cycle

With the growing world population and industrial developments, the supply of energy from an economically feasible and widely available source is important. Biomass gasification is a promising technology that produces lower emissions and allows efficient conversion. The gas obtained from the gasification process, especially in steam gasification, consists of a considerable amount of H2 and is used in fuel cells, especially solid oxide fuel cells (SOFC), to generate electricity. SOFC can convert the chemical energy into electricity and is considered as the most suitable fuel cell type for biomass gasification derived fuels. There are numerous research studies on integrated gasification-SOFC systems in the literature. However, these systems are still under development and studies are being conducted on the appropriate design parameters and operating conditions to achieve high energy efficiency. Modeling of the integrated gasification and SOFC system using the thermodynamic method is the simplest way to determine the process behavior. Nowadays, artificial neural networks (ANN) are one of the most popular modeling methods to represent the thermodynamic based gasification and SOFC systems. In this study, an integrated bubbling fluidized bed gasifier and SOFC model was created to generate data for training the ANN models with Aspen Plus simulation. The ANN models predicted the performance parameters in terms of electrical efficiency, net voltage and current density successfully using the varying operating conditions and 30 different biomass types as input parameters. The results showed that the developed ANN models estimated the output parameters with high accuracy by means of R2 greater than 0.999, MAPE < 0.053 and RMSE < 0.751 for training test and validation data sets. © 2021 Elsevier Lt