Modeling of the nanocrystalline-sized mesoporous zinc oxide catalyst using an artificial neural network for efficient biodiesel production

The sulfonated mesoporous zinc oxide catalyst (SO₃H–ZnO) was hydrothermally fabricated and functionalized by sulfonation to catalyze the palm fatty acid distillate (PFAD) to esters. The effect of different reaction parameters including reaction time, reaction temperature, metal ratio, and calcination temperature was modeled by artificial neural networks (ANNs) to find out the possible relative optimum conditions of the synthesized mesoporous SO₃H–ZnO catalyst for the prediction of the nanocrystalline size. Under the optimized conditions of calcine temperature 700?°C, 18?min reaction time, 160?°C reaction temperature, and 4?mmol of Zn concentration predicted a 56.41?nm size of the mesoporous SO₃H–ZnO catalyst. The acquired model was statistically verified for its utility. The quick propagation model with four nodes in the input layer, six nodes in the hidden layer and one node in the output layer (QP-4-6-1) was chosen as the final model due to its optimum statistical characteristics. Furthermore, the most effective parameter was found to be the zinc concentration whilst the reaction time demonstrated the least influence. The optimized mesoporous SO₃H–ZnO catalyst was further utilized for esterification of PFAD, depicting a high fatty acid methyl ester yield (96.11%). It shows a valuable application for the conversion of discarded oils/fats containing high free fatty acids for the production of renewable green biodiesel.     

Combined use of unsupervised and supervised learning for daily peak load forecasting

In this paper, we have aimed to present a hybrid neural network model for daily electrical peak load forecasting (PLF). Since peak loads usually follow similar patterns, classification of data improves the accuracy of the forecasts. Several factors in peak load, e.g. weather temperature, relative humidity, wind speed and cloud cover, were introduced into the model in order to enhance forecast quality. Most classification attempts in the literature have been intuitive and empty of justification. In this paper, we have proposed a novel approach for clustering data by using a self-organizing map. The Davies–Bouldin validity index was introduced to determine the best clusters. A feed forward neural network (FFNN) has been developed for each cluster to provide the PLF. Eight training algorithms have also been used in order to train the proposed FFNNs. Applying principal component analysis (PCA) decreased the dimensions of the network’s inputs and led to simpler architecture. To evaluate the effectiveness of the proposed hybrid model (PHM), forecasting has been performed by developing a FFNN that uses the un-clustered data. The results proved the superiority and effectiveness of the PHM. Linear regression (LR) models have also been developed for PLF, and the results indicated that the PHM produces considerably better forecasts than those of LR models. Furthermore, the results show that the suggested clustering approach significantly improves the forecasting results on regression analysis too.     

Synthesis of Biodiesel through Catalytic Transesterification of Various Feedstocks using Fast Solvothermal Technology: A Critical Review

The fossil fuel reserves are depleting at a more rapid rate as a result of the population growth and the ensuing energy utilization. Biodiesel is a mixture of fatty acid methyl esters produced from the transesterification of plant oils or animal fats. Moreover, the source of raw materials and manufacturing costs have become the major hurdle in the commercialization of biodiesel; thus, alternative sources such as the use of waste oils and non-edible oils together with biodiesel production techniques have long been considered. Selecting an appropriate feedstock and increasing production yield are two important approaches to decrease the costs of biodiesel production. Typically, biodiesel, which operates with electrical or conventional heating to generate high efficiency of the product, consumes a huge amount of power in a long reaction time. In contrast, chemical reactions speed up by microwave irradiation which results in producing high yields of product in a shorter chemical reaction time. In this extensive article, an effort has been made to review the use of microwave technology including multi-feedstock and recent studies on microwave-assisted heterogeneously catalyzed processes for biodiesel production. The heterogeneous catalyst performance has also been covered, including the measurement of their pysico-chemical properties. The microwave irradiation used for the synthesis of biodiesel is also included. In addition, the reaction variables impacting the transesterification process, such as heating system, microwave power, type and amount of heterogeneous catalyst, oil/methanol molar ratio, reaction time, temperature and mixing intensity, are covered. The final part of this article will cover the details of previously performed work on heterogeneous catalysts. Finally, energy balances for the traditional and microwave-based processes, conclusions, and recommendation on the topic are presented. The aim this article is to focus on recent studies on microwave-assisted heterogeneously catalyzed processes.     

Modeling of a Tubular‐SOFC: The Effect of the Thermal Radiation of Fuel Components and CO Participating in the Electrochemical Process

A mathematical model based on first principles is developed to study the effect of heat and electrochemical phenomena on a tubul solid oxide fuel cell (SOFC). The model accounts fordiffusion, inherent impedance, transport (momentum, heat and mass transfer) processes, internal reforming/shifting reaction, electrochemical processes, and potential losses (activation, concentration, and ohmic losses). Thermal radiation of fuel gaseous components is considered in detail in this work in contrast to other reported work in the literature. The effect of thermal radiation on SOFC performance is shown by comparing with a model without this factor. Simulation results indicate that at higher inlet fuel flow pressures and also larger SOFC lengths the effect of thermal radiation on SOFC temperature becomes more significant. In this study, the H₂ and CO oxidation is also studied and the effect of CO oxidation on SOFC performance is reported. The results show that the model which accounts for the electrochemical reaction ofCO results in better SOFC performance than other reported models. This work also reveals that at low inlet fuel flow pressures the CO and H₂ electrochemical reactions are competitive and significantly dependent on the CO/H₂ ratio inside the triple phase boundary.     

Dynamic risk analysis using alarm databases to improve process safety and product quality: Part I—Data compaction

In most industrial processes, vast amounts of data are recorded through their distributed control systems (DCSs) and emergency shutdown (ESD) systems. This two‐part article presents a dynamic risk analysis methodology that uses alarm databases to improve process safety and product quality. The methodology consists of three steps: (i) tracking of abnormal events over an extended period of time, (ii) event‐tree and set‐theoretic formulations to compact the abnormal‐event data, and (iii) Bayesian analysis to calculate the likelihood of the occurrence of incidents. Steps (i) and (ii) are presented in Part I and step (iii) in Part II. The event‐trees and set‐theoretic formulations allow compaction of massive numbers (millions) of abnormal events. For each abnormal event, associated with a process or quality variable, its path through the safety or quality systems designed to return its variable to the normal operation range is recorded. Event trees are prepared to record the successes and failures of each safety or quality system as it acts on each abnormal event. Over several months of operation, on the order of 10⁶ paths through event trees are stored. The new set‐theoretic structure condenses the paths to a single compact data record, leading to significant improvement in the efficiency of the probabilistic calculations and permitting Bayesian analysis of large alarm databases in real time. As a case study, steps (i) and (ii) are applied to an industrial, fluidized‐catalytic‐cracker. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Rational Loading of Linear Multi-Site Receptors with Functional Lanthanide Containers: The Missing Link between Oligomers and Polymers

Although metal-containing organic polymers are becoming essential for modern applications in lighting, catalysis, and electronic devices, very little is known about their controlled metallic loading, which mainly limits their design to empirical mixing followed by characterization and often hampers rational developments. Focusing on the appealing optical and magnetic properties of 4f-block cations, the host–guest reactions leading to linear lanthanidopolymers already display some unexpected dependence of the binding-site affinities on the length of the organic polymer backbone: a drift usually, and erroneously, assigned to intersite cooperativity. Taking advantage of the parameters obtained for the stepwise thermodynamic loading of a series of rigid linear multi-tridentate organic receptors with increasing length, N = 1 (monomer L1), N = 2 (dimer L2), and N = 3 (trimer L3), with [Ln(hfa)3] containers in solution (Ln = trivalent lanthanide cations, hfa⁻ = 1,1,1,5,5,5-hexafluoro-pentane-2,4-dione anion), it is demonstrated here that the site-binding model, based on the Potts–Ising approach, successfully predicts the binding properties of the novel soluble polymer P2 ^N made up of nine successive binding units . An in-depth examination of the photophysical properties of these lanthanidopolymers shows impressive UV→vis downshifting quantum yields for the europium-based red luminescence, which can be modulated by the length of the polymeric chain.     

Optimizing the mechanical properties of TiO2/PA12 nano-composites fabricated by SLS 3D printing

In this paper, the selective laser sintering process was used to fabricate the TiO₂/PA12 nano-composite parts by considering the parameters of laser power, scanning speed, and TiO₂ content. The response surface methodology was used to improve the mechanical properties of TiO₂/PA12 nano-composite parts. The fracture surface characteristics of the specimens were examined using the scanning and transmission electron microscopy. The results indicated that the increase in laser power from 10 to 15 W improved the tensile strength, modulus, and impact strength of the nano-composite parts because of the fine dispersion of TiO₂ nano-particles. An increase in the scanning speed from 2000 to 3000 mm/s resulted in the reduction of tensile strength and modulus due to lower heat input and consequently incomplete densification of polyamide-12. In addition, the increase of TiO₂ content up to 5 wt% can improve the tensile strength, modulus, and impact strength, but requires increasing the laser power. However, the mechanical properties of the nano-composite parts were enhanced simultaneously at laser power of 12.4 W, scanning speed of 2000 mm/s and TiO₂ content of 1.9 wt%. Moreover, the addition of TiO₂ up to 5 wt% showed a slight influence on thermal stability and crystallinity of the sintered specimens.