Study on Soybean Oil Plant Wastewater Treatment Using the Electro‐Fenton Technique

Due to its low viscosity, soybean oil is commonly used as edible oil. Its production is accompanied by a large amount of wastewater containing hazardous materials like phenolic compounds. In this study, the electro‐Fenton process with response surface methodology was applied for oil removal from wastewater of soybean oil plants. Five independent variables, including reaction time, pH, current density, molar ratio of H₂O₂ to Fe²⁺, and volume fraction of H₂O₂ to wastewater, were investigated for their effects on oil removal. The significant independent variables were optimized by the Box‐Behnken design. The optimum conditions were statistically obtained, and the results were confirmed by experiment. 92.66 % oil removal were experimentally found for the optimum conditions.     

Safety analysis of discrete event systems using a simplified Petri net controller

This paper deals with the problem of forbidden states in discrete event systems based on Petri net models. So, a method is presented to prevent the system from entering these states by constructing a small number of generalized mutual exclusion constraints. This goal is achieved by solving three types of Integer Linear Programming problems. The problems are designed to verify the constraints that some of them are related to verifying authorized states and the others are related to avoiding forbidden states. The obtained constraints can be enforced on the system using a small number of control places. Moreover, the number of arcs related to these places is small, and the controller after connecting them is maximally permissive.     

Geometric optimization for a thermal microfluidic chip

A geometric design for a microfluidic chip using numerical simulation is presented. Finite element method was employed in order to design the microchannel configuration for a microfluidic chip array. The effect of geometry on the thermal response at the interface between the microfluidic chips and an integrated system, such as a micro-electronic device, was investigated. Dimensionless design charts, obtained from the parametric models, demonstrated that a compromise between a maximum heat transfer and a minimum interface temperature was achieved with an equilateral triangle cross-section at a microchannel spacing to width ratio of two. The transient response of the microfluidic chip implied that the transient analysis corresponded to the steady state results under different boundary conditions.     

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme learning machines, including the standard, online sequential, and kernel extreme learning machines, in addition to the artificial neural network, classification and regression tree model, and statistical multiple linear regression model. The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models. The input variability was assessed based on two scenarios prior to the application of the predictive models. For the first assessment, the machine learning models were developed using all the available cement and concrete mixture input variables; the second assessment was conducted based on the gamma test approach, which is a sensitivity analysis technique. Subsequently, the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches. The adopted methodology attained optimistic and reliable modeling results. The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.     

Structural evolution of thermal sprayed bismuth sodium titanate piezoelectric ceramic coatings

The structural evolution of lead-free piezoelectric bismuth sodium titanate (BNT) coatings with excess Bi derived from thermal spray process was investigated with transmission electron microscopy (TEM). Bi-rich composition was identified as the nucleation agent of the BNT perovskite phase, and fine-grained microstructure was obtained with more excess Bi. The Bi-rich composition of the perovskite phase crystallized from melt was Bi_xNa_yTiO₃ (0.5 ≤ X ≤ 0.55, 0.46 ≤ y ≤ 0.5). The crystallization of the Bi-rich BNT perovskite phase first from liquid precursor phase would result in continuous depletion of Bi composition in the residual amorphous phase, and thus Ti-rich amorphous phase in the as-deposited coating and sodium titanate secondary phase in the heat-treated coating formed in the composition without enough access Bi. The results and analyses suggest only appropriate compensation with adequate amount of excess Bi can realize single perovskite phase in thermal sprayed BNT-based coatings and hence the superior piezoelectric performance property.     

Ambiguity-driven fuzzy C-means clustering: how to detect uncertain clustered records

As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input sample to belong to more than one cluster, providing more flexibility than non-fuzzy clustering methods. However, the accuracy of FCM is subject to false detections caused by noisy records, weak feature selection and low certainty of the algorithm in some cases. The false detections are very important in some decision-making application domains like network security and medical diagnosis, where weak decisions based on such false detections may lead to catastrophic outcomes. They mainly emerge from making decisions about a subset of records that do not provide sufficient evidence to make a good decision. In this paper, we propose a method for detecting such ambiguous records in FCM by introducing a certainty factor to decrease invalid detections. This approach enables us to send the detected ambiguous records to another discrimination method for a deeper investigation, thus increasing the accuracy by lowering the error rate. Most of the records are still processed quickly and with low error rate preventing performance loss which is common in similar hybrid methods. Experimental results of applying the proposed method on several datasets from different domains show a significant decrease in error rate as well as improved sensitivity of the algorithm.     

In vitro and in vivo antifungal properties of silver nanoparticles against Rhizoctonia solani,a common agent of rice sheath blight disease

Sheath blight disease in rice has caused major crop losses worldwide. Managing the causal agent of disease Rhizoctonia solani Kühn is difficult because of its broad host range and formation of sclerotia which can survive in harsh environmental conditions; therefore developing innovative disease management methods without application of hazardous chemicals has been considered as the main concern to maintain sustainable agriculture. This presented research has revealed the negative impact of silver nanoparticles (SNPs) on R. solani and disease progress both in vitro and in vivo. The adverse effects of the SNPs on R. solani are significantly dependent on the quantity of SNPs, sprayed at different concentrations in vitro. The highest inhibition level against sclerotia formation and mycelia growth are 92 and 85%, respectively, at a SNPs concentration of 50 ppm. In vivo glasshouse experiments also showed that SNPs at the same concentration favourably affects both the fresh and dry weight of rice plants with a remarkable suppressive effect on the lesion development in leaves.Inspec keywords: antibacterial activity, silver, nanoparticles, nanomedicine, plant diseases, cropsOther keywords: in vitro antifungal properties, in vivo antifungal properties, silver nanoparticles, rice sheath blight disease, major crop losses, causal agent, Rhizoctonia solani Kühn, sclerotia, harsh environmental conditions, disease management methods, hazardous chemicals, sustainable agriculture, negative impact, inhibition level, mycelia growth, in vivo glasshouse experiments, rice plants, suppressive effect, lesion development, leaves     

Modeling and analysis of MEMS disk resonators

Microsystem Technologies - In this work a very accurate process for modeling a microdisk resonator is presented and the dynamic behavior of the resonator is investigated. Using the minimization of...     

Localization of emergency acoustic sources by micro aerial vehicles

For micro aerial vehicles (MAVs) involved in search and rescue missions, the ability to locate the source of a distress sound signal is significantly important and allows fast localization of victims and rescuers during nighttime, through foliage and in dust, fog, and smoke. Most emergency sound sources, such as safety whistles and personal alarms, generate a narrowband signal that is difficult to localize by human listeners or with the common localization methods suitable for broadband sounds. In this paper, we present three methods for MAV‐based emergency sound localization system. The first method involves designing a new emergency source for immediate localization by the MAV using a common localization method. The other two novel methods allow localizing the currently available emergency sources, or other narrowband sounds in general, that are difficult to localize due to the periodicity in the sequence of sound samples. The second method exploits the Doppler shift in the sound frequency, caused due to the motion of the MAV and the dynamics of the MAV to assist with the localization. The third method involves active control of the robot's attitude and fusing acoustic and attitude measurements for achieving accurate and robust estimates. We evaluate our methods in real‐world experiments with real flying robots.