Centre for Automation and Robotics (ANRO)

Wind energy is one of the important renewable energy resources. The wind energy is converted into electrical energy using rotating blades which are connected to the generator. Due to environmental conditions and large structure, the blades are subjected to various faults and cause a lack of productivity. The downtime can be reduced when they are diagnosed periodically using structural health monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using random forest algorithm.

Wind energy is one of the important renewable energy assets as a result of its unwavering quality. The wind energy is converted into electrical energy using rotating blades which are connected to the generator. Due to natural conditions and large structure, the blades are subjected to various faults and cause lack of productivity. The downtime can be reduced when they are diagnosed periodically using condition monitoring technique. The faults like blade bend, blade cracks, blade erosion, hub-blade loose connection and pitch angle twist were considered. Thisstudy is considered as a machine learning problem which consists of three phases, namely feature extraction, feature selection and feature classification. Statistical features were extracted from vibration signals, feature selection was carried out using aJ48 algorithm and the feature classification was performed using nested dichotomy classifiers. A better technique is suggested for condition monitoring of wind turbine blade.

The world is moving towards the generation of electricity with renewable energy sources (RES) due to the deterioration of the green environment and trying to replace non-renewable energy resources. The real-time results are achieved with the help of an arm controller, having good controller efficiency with the Waijung toolbox, compatible with MATLAB using STM32ST-link utility. In this paper, the authors are focused on areas such as easy to implement controller efficiency, and real-time solutions for modified direct-control perturbation & observation (DC-P&O) technique based on 32- bit ARM Cortex microcontroller (STM32F407VGT6) with embedded programming using Waijung blocksets, which offers very expected outcomes of the problem to make the stand-alone system efficient with fast-tracking. The observation setup is tested with a 40-watt photovoltaic (PV) panel with resistive load for achieving its stability. The designed algorithm enhances the efficiency of the controller by 84.48% for ...

Wind energy is one of the rapidly evolving renewable energy resources. It is particularly essential to built dependability and accessibility of wind turbines and additional to decrease the wind energy cost. Wind turbine blades are ought to be an important component among the other basic segments in the wind turbine framework since they transform the dynamic energy of wind into useable power. Wind turbine blades are manufactured from either carbon fiber reinforced polymer or glass fiber reinforced polymer. Damages and flaws are unavoidable either in the manufacturing process or the lifetime of a composite blade. Hence, structural health monitoring for wind turbine blade is essential to avoid failures and extend dependability in both fabrication quality control and in-service investigation. In this study, a three-bladed variable wind turbine was chosen and using histogram features, the condition of a wind turbine blade is inspected. The faults like hub-blade loose connection, blade cr...

The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the v...

The analysis of different components of a grid-linked hybrid energy system (HES) comprising a photovoltaic (PV) system is presented in this work. Due to the increase of the population and industries, power consumption is increasing every day. Due to environmental issues, traditional power plants alone are insufficient to supply customer demand. In this case, the most important thing is to discover another approach to meet customer demands. Most wealthy countries are now concentrating their efforts on developing sustainable materials and investing considerable amounts of money in product development. Wind, solar, fuel cells, and hydro/water resources are among the most environmentally benign renewable sources. To control the variability of PV generation, this sort of application necessitates the usage of energy storage systems (ESSs). Lithium-ion (Liion) batteries are the most often used ESSs; however, they have a short lifespan due to the applied stress. Hybrid energy storage systems (HESSs) started to evolve as a way to decrease the pressure on Li-ion batteries and increase their lifetime. This study represents a great power management technique for a PV system with Li-ion batteries and supercapacitor (SC) HESS based on an artificial neural network. The effectiveness of the suggested power management technique is demonstrated and validated using a conventional PV system. Computational models with short-term and long-term durations were used to illustrate their effectiveness. The findings reveal that Li-ion battery dynamical stress and peak value are reduced, resulting in longer battery life.

This study aimed to acquire a balance of mechanical properties comprising impact, tensile and flexural performances in PP based blend. In this respect, co-PP was employed as matrix because of its intrinsic high impact behavior. Hybrid nanocomposites based on co-PP and containing 10 wt % micron-sized short glass fibers (GF) and 2 to 8 wt % nano precipitated CaCO 3 (NPCC) particles were produced by applying a two-step melt compounding method. Maleic anhydride grafted polypropylene (MAPP) was used as compatibilizer. Strong glass fiber-matrix adhesion and relatively uniform distribution of nano-CaCO 3 particles were observed in SEM images. The maximum tensile strength was observed in co-PP hybrid nanocomposite containing 10 wt % glass fiber and 5 wt % nano-CaCO 3 which was 58% more than that of neat co-PP. Flexural strength raised as much as 11% by adding glass fiber. The maximum flexural strength was obtained by incorporating 10 wt % glass fiber and 8 wt % nano-CaCO 3 into co-PP matrix which was 24% higher than that of neat co-PP. The impact strength decreased upon addition of 10 wt % glass fiber and 5 and 8 wt % nano-CaCO 3 , this was attributed to the inherent high impact behavior of co-PP as well as strong interfacial interaction between dispersed phases and polymeric matrix.

The global population growth has led to a considerable rise in demand for wheat. Today, the amount of energy consumption in agriculture has also increased due to the need for sufficient food for the growing population. Thus, agricultural policymakers in most countries rely on prediction models to influence food security policies. This research aims to predict and reduce the amount of energy consumption in wheat production. Data were collected from the farms of Estahban city in Fars province of Iran by the Jihad Agricultural Department’s experts for 20 years from 1994 to 2013. In this study, a novel prediction method based on consumed energy in the production period is proposed. The model is developed based on artificial intelligence to forecast the output energy in wheat production and uses extreme learning machine (ELM) and support vector regression (SVR). In the experimental stage, the value of elevation metrics for the EVM and ELM was reported to be equal to 0.000000409 and 0.953...

Solar energy has been used to preserve agricultural products which require drying through a consistent application of relatively low heat. In the present study, the feasibility of implementation the solar dryer system in the city of Yazd in Iran is evaluated by examining the effective factors. By identifying factors which may somehow affect the design, construction and operation of solar drying systems, a questionnaire was prepared and distributed. Among the results of the analysis, six main factors were identified: "performance," "geographical location," "infrastructures," "interactions," "financial support" and "social, cultural and political issues." According to the results obtained from factor analysis and with Structural Equations Modelling (SEM) method, a model was introduced. Subsequently, the final model was developed with the use of path analysis method and with AMOS. Based on the final model, the three factors of infrastructures (with the coefficient of 0.44), interactions and cultural, social and political issues (both with the coefficient of 0.12) directly affect the possibility of implementing the solar dryer in Yazd. According to the proposed model, it was concluded that authorities should mainly focus their attentions on finance, science, economy and infrastructures to develop and promote use of solar drying systems.

One major concern about foreign direct investment (FDI) is the potential negative environmental impact due to increased CO2 emissions. However, there is a possibility that FDI mitigates CO2 emissions through green innovation and creates a cleaner environment. In the existing literature, there is no significant empirical evidence on the linkage among FDI, green innovation and CO2 emissions in the context of BRICS countries. Hence, this study aims to analyze the impact of FDI and green innovation on the environmental quality of BRICS economies for 1990–2014. The study employed Augmented Mean Group (AMG) estimators for empirical data analysis. The study’s findings depict that foreign direct investment, energy use, and economic growth have a significant and positive impact on the CO2 emissions of BRICS economies. Moreover, green innovation has a significant inverse impact on CO2 emissions. The results show bidirectional causalities between CO2 emissions and green innovation, trade openn...

The present work focuses on the effect of flutter in prebend 100 m horizontal axis wind turbine blade (HAWT) within the stability limits. The study was carried out with an advanced beam model for idyllic structure in a DU-97-W-300 cross-sectional area. A Galerkin type of approach has been applied to derive the equations, and the analysis was performed using a standard FEA code which involves the PK method and double lattice method for calculating flutter solution and aerodynamic loads respectively. The results reveal the significance of inducing prebending to improve the stability of the blade structures, and hence, the flutter velocity has moved from 11 m/s to 23 m/s. Furthermore, the output highlights the effect of prebending on the structural stability and also the flutter limit was found to be lengthened.

Several studies have concentrated on cooling the PV module temperature (TPV) to enhance the system’s electrical output power and efficiency in recent years. In this review study, PCM-based cooling techniques are reviewed majorly classified into three techniques: (i) incorporating raw/pure PCM behind the PV module is one of the most straightforward techniques; (ii) thermal additives such as inter-fin, nano-compound, expanded graphite (EG), and others are infused in PCM to enhance the heat transfer rate between PV module and PCM; and (iii) thermal collectors that are placed behind the PV module or inside the PCM container to minimize the PCM usage. Advantageously, these techniques favor reusing the waste heat from the PV module. Further, in this study, PCM thermophysical properties are straightforwardly discussed. It is found that the PCM melting temperature (Tmelt) and thermal conductivity (KPCM) become the major concerns in cooling the PV module. Based on the literature review, expe...

Diesel-powered transportation is considered an efficient method of transportation; this sees the increase in the demand for the diesel engine. But diesel engines are considered to be one of the largest contributors to environmental pollution. The automobile sector accounts for the second-largest source for increasing CO2 emission globally. In this experiment, a suitable postcombustion treatment to control CO2 emission from IC engine exhaust is developed and tested. This work focuses to control CO2 emission by using the chemical adsorbent technique in diesel engine exhaust. An amine-based liquid is used to adsorb the CO2 molecules first and absorb over the amines from the diesel engine exhaust. Three types of amino solutions (L-alanine, L-aspartic acid, and L-arginine) were prepared for 0.3 mole concentrations, and the CO2 absorption investigation is performed in each solution by passing the diesel exhaust. A suitable CO2 adsorption trap is developed and tested for CO2 absorption. Th...

Due to the increased demand for global transportation needs, a long-range diesel engine is considered an important prime mover to fulfill the transportation demand. The major problem addressed by the diesel engines is it liberates harmful emissions and it also increases global warming. CO2 is considered an important greenhouse gas and it has to be controlled by diesel engines. In this research, extensive experimental work is done to identify a suitable solution to control CO2 emissions. For five different mole concentrations (0.1 to 0.5), three types of amino solutions (L-alanine, L-aspartic acid, and L-arginine) were prepared. By passing diesel exhaust through each solution, CO2 absorption is investigated. For CO2 absorption, an appropriate CO2 control system is built and tested. The tests were carried out in a diesel engine which is a naturally aspirated single-cylinder engine with a water-cooled system. It was possible to obtain an effective CO2 reduction of up to 90%, as well as...

Nowadays, for many industries, innovation revolves around two technological improvements, Artificial Intelligence (AI) and machine learning (ML). ML, a subset of AI, is the science of designing and applying algorithms that can learn and work on any activity from past experiences. Of all the innovations in the field of ML models, the most significant ones have turned out to be in medicine and healthcare, since it has assisted doctors in the treatment of different types of diseases. Among them, early detection of breast cancer using ML algorithms has piqued the interest of researchers in this area. Hence, in this work, 20 ML classifiers are discussed and implemented in Wisconsin’s Breast Cancer dataset to classify breast cancer as malignant or benign. Out of 20, 9 algorithms are coded using Python in Colab notebooks and the remaining are executed using the Waikato Environment for Knowledge Analysis (WEKA) software. Among all, the stochastic gradient descent algorithm was found to yiel...

Renewable wind power is productive and feasible to manage the energy crisis and global warming. The wind turbine’s blades are the essential components. The dimension of wind turbine blades has been increased with blade sizes varying from approx. 25 m up to approx. 100 m or even greater with a specific purpose to increase energy efficiency. While wind turbine blades tend to be highly stressed by environmental conditions, the wind turbine blade must be constantly tested, inspected, and monitored for wind turbine blades safety monitoring. This research presents a methodology adaptation on machine learning technique for appropriate classification of different failure conditions on blade during turbine operation. Five defects were reported for the diagnosis study of defective wind turbine rotor blades, and the considered defects are blade crack, erosion, loose hub blade contact, angle twist, and blade bend. The statistical features have been drawn from the recorded vibration signals, and...

Industrial wastes contain more toxins that get dissolved in the rivers and lakes, which are means of freshwater reservoirs. The contamination of freshwater leads to various issues for microorganisms and humans. This paper proposes a novel method to remove excess copper from the water. The nanotubes are used as a powder in membrane form to remove the copper from the water. The multiwalled carbon nanotube is widely used as a membrane for filtration. It contains many graphene layers of nm size that easily adsorbs the copper when the water permeates through it. Activated carbon is the earliest and most economical method that also adsorbs copper to a certain extent. This paper proposes the methods of involving the activated carbon in the multiwalled carbon nanotube to improve the adsorption capability of the copper. Here, activated carbon is impregnated on the multiwalled carbon nanotube’s defect and imperfect surface areas. It makes more adsorption sites on the surface, increasing the a...

Ti-6Al-4V is known for its lightweight, high tensile strength, exceptional corrosion resistance, and low thermal coefficient of expansion due to which it finds its application in precision surgical instruments and aeronautical and marine engine parts. In this study, Ti-6Al-4V has been machined using wire EDM machine with two different wire materials. The wire thickness of 0.25 mm heat-treated brass and zinc-coated brass tool and super cool oil as dielectric fluid were utilized for the machining process. Taguchi L9 orthogonal array was used. Ton (Pulse ON time), Toff (Pulse OFF time), wire feed, and Ip (Current) were the input parameters, and the response parameters such as surface roughness (Ra), material removal rate (MRR), tool wear rate (TWR), and recast layer thickness were optimized by TOPSIS optimization. From the results, higher tool wear rate was observed in brass tool. The titanium percentage after machining was found to be 55.05% for zinc-coated brass wire, whereas it was ...

The base fluids of eco-friendly cutting fluids should contain some vegetable oil. Due to their eco-friendly, thermal, and tribological qualities, these types of machining fluids are becoming increasingly popular among researchers throughout the world. The authors summarized published research studies on the use of eco-friendly machining fluids (including nanofluids) in various metal removal processes such as turning, milling, grinding, and drilling in this review paper. The impact of several biodegradable oil-based machining fluids on performance metrics such as surface irregularity, machining force, tool wear, power demand, and temperature-induced during the machining process is also explored in this paper. According to the previous and current literature, using an eco-friendly nano machining fluid results in better surface features, less cutting tool wear, lower cutting force, less power required, and lower machining zone temperature during the machining operation due to superior ...