Biodiesel can be produced through various methods, but the transesterification reaction is the most widely used due to its advantages, such as improved biodiesel quality, continuous in-line processing, reduced methanol and catalyst requirements, and enhanced energy efficiency. Recent advancements in biodiesel production technology have focused on optimizing the mixing process and improving mass and heat transfer between the two liquid phases involved in the transesterification reaction. These innovations have led to the development of new reactors that significantly increase reaction rates and reduce production time. In this study, the design and construction of a novel hydrodynamic cavitation reactor—a commercially viable renewable energy system—were investigated. After its construction and commissioning, the reactor was tested using standard biodiesel production materials. The evaluation results demonstrated that the optimal reaction time for biodiesel production was 3.13 minutes, with a rotational speed of 16,000 rpm and a flow rate of 0.83 liters per minute. The biodiesel produced met high-quality standards and complied with international fuel specifications. The highest hydrodynamic cavitation efficiency achieved was 6.19 mg/kJ. The results indicated that the transesterification reaction efficiency exceeded 88% at 3.13 minutes using this hydrodynamic cavitation reactor, highlighting an excellent recovery time for crude biodiesel production. This method significantly reduces processing time compared to conventional biodiesel production reactors, which typically require more than 20 minutes to over an hour to complete the process. The successful design, commissioning, and biodiesel production using this reactor represent a significant step forward in intensifying and optimizing the biodiesel production process.

The objective of this study was to identify and classify adulteration in certain fossil fuel products (including gasoline, diesel, and kerosene) using an electronic nose (e-nose). In this research, blends of gasoline-diesel, gasoline-kerosene, and diesel-kerosene were prepared at volumetric ratios of 5%, 10%, 15%, 20%, 25%, and 30%. Data acquisition was performed using an e-nose system equipped with 10 metal oxide semiconductor (MOS) sensors. The collected data were analyzed using various methods, including Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Among the sensors, MQ135, TGS2611, and TGS2610 demonstrated the highest performance. The results showed that the identification and classification of pure fuels using QDA, SVM, and ANN achieved 100% accuracy, while the LDA method achieved 98.8% accuracy in distinguishing pure fuel types. Based on the results, the e-nose system demonstrated over 90% accuracy in detecting and classifying fuel adulteration, outperforming traditional methods based on conductivity and pH measurements.

Tomato is a major horticultural crop consumed globally. As a widely used ingredient in foods, tomato paste's quality and safety are highly important. This study investigates adulteration in tomato paste using an electronic nose (E-nose) system equipped with 10 gas sensors to detect sodium benzoate and potassium sorbate at concentrations of 0%, 0.05%, and 0.1%. The effects of these preservatives on physicochemical properties, including pH, total soluble solids (TSSs), precipitate weight ratio (PPT), and total acidity (TA), were also evaluated. Results showed that both preservatives significantly affected all physicochemical parameters at the 1% significance level (p < 0.01) across 50 tested samples. Linear discriminant analysis (LDA) achieved 100% accuracy in detecting preservative levels, while principal component analysis (PCA) and support vector machine (C-SVM) effectively identified samples with 0.05% and 0.1% preservatives. The loading chart highlighted TGS2620, MQ135, and TGS2602 as the most sensitive sensors for detecting adulterated samples. Principal component regression (PCR) and multiple linear regression (MLR) were the most effective models for predicting TA and TSS, PPT, and pH, respectively. The E-nose proved to be an efficient tool for detecting adulteration in tomato paste.

Introduction The use of corn oil in diets is due to its positive effects on cardiovascular and immune systems. Corn oil is composed of 99% triacylglycerol, with 59% unsaturated fatty acids and 13% saturated fatty acids. Of the unsaturated fatty acids, 24% contain a double bond. Because of this composition, corn oil can be a good alternative to other oils high in saturated fatty acids, as it reduces blood cholesterol levels.This study employed an electrical nasal system to detect the amount of palm oil present in corn oil. The properties extracted from the signals obtained by the device were processed using principal component analysis, artificial neural networks, infusion, and response surface methods. The results were then compared to find the best method for detecting palm oil levels in corn oil.Materials and Methods The required palm oil was obtained from the Nazgol Oil Agro-industrial Plant, while the corn oil was obtained from natural lubrication centers. To prepare samples with different percentages of palm oil, 75 grams of palm oil and corn oil with the specified percentages were mixed and stored in special containers. In the electrical nose system, ten metal oxide semiconductor sensors (MOS) were used to collect output data. Pre-processing operations were performed on this data using RSM, ANFIS, PCA, and ANN methods to estimate the percentage of palm oil in corn oil. The Unscrambler V.9 software, Design Expert 8.07.1, and MATLAB R2013a were used to analyze the results.Results and Discussion Based on the Score plot, PC-1 and PC-2 explain 53% and 25%, respectively, describing the variance between samples for a total of 78 data points. The analysis indicates that sensors 7 and 8 have minimal impact on the detection process and can be removed from the sensor array. When reducing the cost of the olfactory system's sensor array, sensor 6 plays a more significant role than other sensors in detecting corn oil with palm composition.According to the loading diagram of palm percentage in corn oil, the MQ6 sensor had the least effect in classifying different percentages of palm in corn oil and pattern identification. Out of all functional parameters (accuracy, sensitivity, and specificity), the RSM method is deemed more appropriate for determining the percentage of palm in corn oil.Regarding the separation of corn oil and palm oil by ANFIS, RSM, and ANN, the results in Table 3-1 indicate that the RSM method is better suited for classifying corn and palm oil.Conclusion In this study, we used an electronic multi-sensor system based on metal oxide sensors to analyze various aromatic compounds in different oil and palm samples and to detect the presence of palm. The system provided comparable information for classifying different samples of palm oils. Using PCA, ANN, ANFIS, and RSM methods, we evaluated the system's performance in differentiating and classifying various oil and palm samples.The results obtained from the loading diagrams for the detection of palm in corn oil indicated that the MQ6 sensor had the least impact on the detection process. Therefore, this sensor can be removed from the sensor array.Additionally, our analysis showed that using the RSM method is more effective in detecting different percentages of palm in corn oil. Overall, our study demonstrates the efficacy of the electronic multi-sensor system in analyzing different oil and palm samples and detecting the presence of palm.

Biodiesel is produced from renewable sources such as vegetable oils and animal fats as a fuel for replacing fossil fuels in diesel engines. Energy efficiency and energy intensity in production processes are a significant issue. In this research work, the process energy indices and economic analysis have been investigated due to determine the potential advantages of using microwave energy for waste fish oil biodiesel production. A flow process including a magnetic stirrer and a microwave reactor has been used for transesterfication reaction. The equivalent of input and output energy of the biodiesel production process and the energy indices were obtained. According to the results, the total amount of input and output energy was 48.839 and 50.866 MJ.L-1, respectively. The values of energy ratio, energy efficiency and energy intensity for producing one liter of biodiesel were calculated 1.0415, 0.0178 kg.MJ(-1) and 56.0724 MJ.kg(-1). Renewable and non-renewable energy values were obtained at 41.962 and 6.877 MJ.L-1, respectively. Economic efficiency was also achieved at 1.6637 kg.$(-1). In other words, in this work, the price of produced biodiesel per gasoline-gallon equivalents (GGEs) will be 3.72 USD.

The aim of this study was to determine the authenticity of honey by processing microscopic images and obtaining an algorithm for classifying various honey frauds. In this study, sucrose, fructose, and fructose-glucose solution at a ratio of 0.9 were used to make honey adulteration. The level of adulterated honey was based on the weight percentages of 2.5, 5, 7.5, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 by stirring. Different samples were imaged under a microscope. Each image was processed in 33 monochrome color spaces and 15 parameters were extracted from it. The three main and effective parameters of various color spaces were selected using sensitivity analysis for modeling honey fraud by adaptive Fuzzy Neural Inference System (ANFIS), Artificial Neural Network (ANN), and response surface methodology. Various criteria were used to evaluate the performance of the models such as coefficient of determination, mean square error, sum of squared estimate of errors, and mean absolute errors. The results showed that the determination coefficient and the mean square error of the artificial neural network model was 0.974 and 0.0024, respectively. Finally, using the desirability function, the artificial neural network model was selected as the best model due to less prediction error values and desirability of 0.948.

Disposal of medical waste (MW) must be considered as a vital need to prevent the spread of pandemics during Coronavirus disease of the pandemic in 2019 (COVID-19) outbreak in the globe. In addition, many concerns have been raised due to the significant increase in the generation of MW in recent years. A structured evaluation is required as a framework for the quantifying of potential environmental impacts of the disposal of MW which ultimately leads to the realization of sustainable development goals (SDG). Life cycle assessment (LCA) is considered as a practical approach to examine environmental impacts of any potential processes during all stages of a product's life, including material mining, manufacturing, and delivery. As a result, LCA is known as a suitable method for evaluating environmental impacts for the disposal of MW. In this research, existing scenarios for MW with a unique approach to emergency scenarios for the management of COVID-19 medical waste (CMW) are investigated. In the next step, LCA and its stages are defined comprehensively with the CMW management approach. Moreover, ReCiPe2016 is the most up-to-date method for computing environmental damages in LCA. Then the application of this method for defined scenarios of CMW is examined, and interpretation of results is explained regarding some examples. In the last step, the process of selecting the best environmental-friendly scenario is illustrated by applying weighting analysis. Finally, it can be concluded that LCA can be considered as an effective method to evaluate the environmental burden of CMW management scenarios in present critical conditions of the world to support SDG.

Abstract Tomato is the second most important product consumed worldwide. Since tomato paste is considered the most important seasoning in food, its quality and safety are of particular importance. The present study aims to investigate adulteration in tomato paste. To this aim, an olfactory machine system based on 10 gas sensors was used, and its potential to detect different levels of adulteration of sodium benzoate and potassium sorbate at 0, 0.05, and 0.1% in tomato paste was assessed. The effect of these preservatives on some physicochemical properties of tomato paste, such as pH, total soluble solid (TSS), precipitate weight ratio, and acidity, was also investigated. The results indicated that both preservatives in tomato paste significantly affected all its physicochemical parameters at the 1% significance level. The confusion matrix results showed that the LDA method has a high performance in detecting different percentages of preservatives with 100% accuracy. C-SVM and PCA were also recognized as suitable and accurate methods for detecting samples containing sodium benzoate and potassium sorbate at 0.05% and 0.1%. The results of the loading chart indicated that TGS2620, MQ135, and TGS2602 sensors were the most sensitive sensors in detecting adulterated tomato paste samples. Also, according to the results of predicting physicochemical parameters, PCR and MLR models were the most appropriate models to predict acidity and precipitate weight ratio, Brix, and pH, respectively. Overall, it can be stated that the electronic nose is an appropriate tool to detect food adulteration in all adulteration types, thereby saving substantial time and money.

The main purpose of the present paper was to investigate, weigh, and prioritize criteria, indicators, and options aiming to protect Zagros forests in Kermanshah province. We focused on developing a preferred prioritizing approach based on the fuzzy version of the technique for order of preference by similarity to ideal solution (namely, fuzzy ideal responses) (FTOPSIS). The proposed method was intended to identify forest protection options, as well as to evaluate standards for Zagros forest protection through the fuzzy analytical hierarchy process (FAHP) and fuzzy Delphi method. The present study was descriptive-practical in terms of the research type and quantitative in terms of implementing a fuzzy multi-criteria decision-making method. A statistical sample used in this study comprised experts and university researchers in the field related to natural resource protection, including 54 people in total; the applied research tool was a questionnaire. Data analysis was performed using the MATLAB software, Lingo, and the nonlinear programming model (2004), which was applied to evaluate the efficiency of the options aimed at protecting the forests of Kermanshah province by using FTOPSIS and prioritizing the criteria through FAHP. Based on the results of prioritizing the options by using FTOPSIS, the empowerment option of local communities with a relative proximity index, namely, commodity channel index (CCI), equal to 0.599, was identified as the most important option in terms of protecting forest in Kermanshah province. From the viewpoint of the expert team, the collaborative protection option and the training and promotion approach with the values of CCI equal to 0.559 and 0.521 were ranked as the second and third, respectively. Then, it was noted that watershed operation, protection and conservation, patrolling and maintaining natural resources, supplying fuel to forest dwellers, outsourcing policies, supplying manpower and equipment for forest protection, and modification of the forest protection structure were ranked in a descending order. The results of FAHP based on the nonlinear programming model using Lingo software indicated that the technical criterion with the weight of 0.192 had the first priority among others. The social criterion with the weight of 0.188 was assigned the second priority, and the preventive criterion with that of 0.179 obtained the third priority.

Pomegranate acidity is one of the important characteristics of this fruit because it determines its uses. By figuring out the pH of the pomegranate, the consumers can choose the fruit appropriate to their needs and tastes. In this study, the pH of 200 pomegranates was measured, and according to the properties extracted from the pomegranates' images, their pH was found to be not destructive. By processing pomegranate images and measured characteristics and using sensitivity analysis, the researchers identified four parameters that had the most effects on pH changes. The properties of pomegranate crown were also used for this purpose. With the help of three algorithms of artificial intelligence-the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and response surface methodology (RSM)-a model was designed for the estimation of the pH of the pomegranate. The best result was obtained by the ANFIS model, in which the R(2)and MSE values were equal to 0.984 and 0.202.

Solar energy is one of the clean and healthy energies. Due to the high cost of required equipment to convert solar energy into the desired form, the economic facets must be addressed and the equipment should be installed in areas with higher accessible solar energy. However, due to the complex and time-consuming process of calculating solar radiation, it seems necessary to develop more simple models with higher estimation capability. Therefore, the present study investigated the prediction of solar radiation on the horizon using neural network methods, ANFIS and RSM, in Sarpol-e-Zahab Township, Kermanshah, Iran. In this respect, the meteorological data of this township were collected. Then, the key parameters were selected by performing sensitivity analysis, and models were designed and optimized using ANFIS, ANN, and RSM methods. Moreover, respective correlation coefficients and mean square errors of each method were obtained (ANFIS (0.993 and 0.0005), ANN (0.996 and 0.00029), and RSM (0.996 and 0.00027), respectively). Also, the neural network and response surface methodology were superior to the ANFIS Model in terms of performance, simplicity, and speed. In short, the performance of the response surface methodology was slightly better than that of the neural network.

This research utilized a combined hydrodynamic cavitation reactor to produce biodiesel. The reactor worked automatically with the help of a controller designed by LabVIEW. For this purpose, rapeseed oil (0.5 L per experiment) and methanol alcohol with the sodium hydroxide catalyst were used for biodiesel production. The important factors of the study were: 1.pump flow rate (three levels of 1.4, 2 and 2.6 L/min); 2.the molar ratio of methanol to oil (4:1, 6:1 and 8:1); 3.the rotational speed of the reactor (8000, 12000 and 16000 rpm), and 4.circulation time (2, 4 and 6 min). The study analyzed the energy ratio (output energy/input energy) of the produced biodiesel to evaluate the system and modeled the performance of the system to obtain the best-operating conditions of the reactor. In this respect the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and response surface methodology (RSM) methods were employed. The average energy ratio was obtained 1.205, and the R2 of the best ANFIS, ANN and RSM models were 0.989, 0.966, and 0.990, respectively, and MSE was calculated at 0.0005, 0.0015 and 0.00003. The results revealed that the RSM and ANFIS models were preferred to the neural network model in terms of better performance, simplicity, and high processing speed. In general, the RSM model functioned better than the ANFIS model. Accordingly, the best reactor settings to obtain the maximum energy ratio (1.35) and biodiesel yield (91.87 %) was when the circulation time, the rotational speed, the pump flow rate and the molar ratio were set at 2 min, 8000 rpm, 1.4 L/min and 4, respectively.

Pomegranate fruit are usually separated and graded manually. Also, color and size of the arils, are not measurable without removing the fruit peel. The objective of this study was to develop a method for grading pomegranate fruit based on color and size of arils using image processing and artificial intelligence. The physical characteristics of 200 fruit were measured and photographed, the fruit peels were cut, the arils photographed, and then categorized into three grades by an expert. The images were processed and modeled using three artificial intelligence algorithms. An Artificial Neural Network (ANN) Model with an accuracy of 98%, a correlation coefficient of 0.943 and a MSE of 0.008 was recognized as optimal. Accuracy of 95.5% and 75.5% was obtained for Adaptive Neuro Fuzzy Inference System (ANFIS) and Response Surface Methodology (RSM), respectively. The correlation coefficient and MSE of the ANFIS Model was 0.918 and 0.011, respectively while the RSM Model had a correlation coefficient of 0.622 and a MSE of 0.052.

Given the significance of the relationships between human beings, environment, machines and ergonomics as well as the necessity of using renewable fuels, the present study aimed to investigate the effects of different blends of biodiesel, bioethanol and diesel on noise pollution emitted by a MF285 tractor in stationary and moving modes by the aid of statistical methods. In this respect, the emitted noise was measured using the noise dosimeters and sound level meters in the driver and the bystander’s positions, at 1000, 1600 and 2000 RPM in both stationary and moving modes. Then, nine fuel blends of biodiesel, bioethanol and diesel with different volumetric percentages as well as pure diesel were studied. To study the effects of key factors on noise pollution, the factorial experiment was conducted in the form of a completely randomized design, followed by the application of the SPSS Statistics Software Version 19.0. The fuel type nearly affected the noise pollution at the level of 5%, and other factors such as engine rotational speed and the fuel type-engine rotational speed interaction influenced it at the level of 1%. In both the driver and bystander’s positions, the minimum and maximum noise pollution occurred at 1000 and 2000 RPM, respectively. The effects of gears along with their twofold and threefold interaction with other factors were not significant. Finally, the results of the present study demonstrated that the B25E4D71 fuel, composed of 25% biodiesel and 4% bioethanol, had the lowest noise pollution.

Since seafood is highly susceptible to corruption, it is important to check their storage and shelf-life time. In this research, image processing technology was used to recognize the freshness (time lasted of catching) of shrimps. Shrimp samples were randomly selected from shrimp farming pools and stored in three storage conditions: freezer, refrigerator, and cool environments. Images were taken from the samples at intervals of two hours in a controlled environment for more than a month. Finally, 482 properties were extracted from each image. Three effective parameters for modeling were selected by sensitivity analysis. The time that lasted from catching was the output of the models. Modeling was performed using ANFIS, ANN, and RSM algorithms. In the modeling, the R2 values of the ANN algorithm with 0.987006, 0.987009, 0.984484, and 0.976001 were the best model for storing conditions: freezer, refrigerator, cooler environments and the total of storage conditions, respectively. All three modeling methods can estimate the catching time with high accuracy. But the ANN model was recognized as the best one according to the remaining diagram and the values of R2 and MSE.

In this study, the application of a versatile approach for modeling and prediction of the moisture content of dried savory leaves using hybrid artificial neural network-genetic algorithm has been presented. Genetic Algorithm was used in order to find the best Feed Forward Neural Network (FFNN) structure for modeling and estimation of moisture content in the drying process of savory leaves. The experiments were performed at three air temperatures of 40, 60 and 80 °C and at three levels of relative humidity 20%, 30% and 40% and air velocity of 1, 1.5 and 2.0 m/s for drying the savory leaves in the forced conductive dryer. Optimized neural network by GA had two hidden layers with 9 and 17 neurons in first and second hidden layers, respectively. Mean Square Error (MSE) value (0.000094606) and correlation coefficient (0.9992) of FFNN-GA experiments showed that moisture content can be accurately predicted from the input variables: air temperature, airflow velocity, relative humidity and drying time. Moreover, results showed that the optimized neural network topology could denote the superior ability of this intelligent model for on-line prediction of the moisture content of Savory leaves in different drying conditions.

The purpose of the present study is to produce biodiesel from fish waste oil and methanol by combination of mechanical stirrer and microwave as a technique to accelerate this. In this research, a microwave system was used including microwave source, stirrer, spiral tube and decantor. With the help of this system, the effect of molar ratio of alcohol to oil (4 to 1, 6 to 1 and 8 to 1), catalyst concentration (0.5, 1 and 1.5 weight percent of oil), reaction time (5, 15 and 25 min) and microwave time (0.5, 1.5 and 2.5 min) on the conversion of fatty acid to methyl ester. For analyzing the obtained results, the response surface method and Box Behnken layout were used in Design Expert 10.0 software. After analyzing the data and optimizing the biodiesel production reaction, it was found that the highest percentage of biodiesel conversion (92.62%) was found in the catalyst concentration of 1.13%, the reaction time of 24.61, the molar ratio of alcohol to oil of 5.55 and the microwave time of 0.5 minute. The regression model between independent variables and dependent variable (percent conversion) was obtained as a quadratic equation with R2 = 0.9953.