Dr Prabhat Ranjan
@pvpittssm.edu.in
Assistant Professor
PVPIT, PUNE
RESEARCH INTERESTS
FLUID POWER
Scopus Publications
Scopus Publications
S M Mozammil Hasnain, Rajeshwari Chatterjee, Prabhat Ranjan, Gaurav Kumar, Shubham Sharma, Abhinav Kumar, Bashir Salah, and Syed Sajid Ullah
MDPI AG
The demand for sustainable alternative-fuels in the transportation and agriculture domains is essential due to the quick depletion of petroleum supplies and the growing environmental challenges. The ternary-blends (diesel, biodiesel, and Methyl oleate) have the ability to report the existing challenges in this area because they offer significant promise for reducing exhaust emissions and improving engine performance. In the current work, soy methyl ester is blended with methyl oleate and diesel. The emissions and performance of blended biodiesel was conducted in common rail direct injection engine (CRDI). The characterization and physical properties were also evaluated by utilizing various methods like Fourier-Transform Infrared Spectroscopy (FTIR), UV-vis Spectroscopy (UV-vis), and Nuclear Magnetic Resonance. FTIR spectra showed the existence of the strong C=O, indicating the presence of FAME at 1745 cm−1. Again, UV-vis has reported the appearance of conjugated dienes in the oxidized biodiesel. The results indicated all blended samples retained the properties of diesel. The addition of methyl oleate improved brake specific fuel consumption of blended biodiesel almost near to diesel. D50::S80:M20 produced a mean reduction in hydrocarbon 42.64% compared to diesel. The average carbon monoxide emission reduction for D50::S80:M20 was 49.36% as against diesel.
Gajala Praveen, Piyush Kumar Singh, and Prabhat Ranjan
Inderscience Publishers
Gajala Praveen, Mayank Anand, Piyush Kumar Singh, and Prabhat Ranjan
Springer Singapore
Prabhat Ranjan, Mohit Bhola, Gyan Wrat, Santosh Kr. Mishra, and Jayanta Das
SAGE Publications
In this article, the heavy earth moving machinery like hydraulic excavator used in the construction and mining industries has been taken into consideration. Most of the heavy earth moving machineries used in these industries are mobile with engine as the main source of power. The work deals with two different hydraulic circuits: first the proposed one and second the conventional one, and both are studied at laboratory scale, which resembles the circuit of the hydraulic excavator. In the hydraulic circuit, out of the three hydraulic linear actuators, two are connected with hydro-pneumatic accumulators and one hydro-motor is also taken into consideration, which resembles the boom, arm, bucket and swing motor, respectively. The idea behind the proposed circuit is to store substantial potential energy during downward movement of the two linear actuators in the accumulators, which resembles the boom and arm cylinder of the excavator. The stored energy is used for upward motion of the two actuators without utilizing any energy from the main pump. For the devised strategy, MATLAB/Simulink environment has been utilized for developing the simulation model and the same has been validated with the experimental data with reasonable accuracy. The effect of accumulator volume and pre-charge pressure has been studied for optimization of the accumulator size on the validated simulation model. This concept helps in saving 14.06% energy than its conventional counterpart which does not have the energy storage elements. The linear position control of the boom and the arm actuator in the proposed circuit have been accomplished using proportional–integral–derivative control and pressure-compensated proportional flow control valve and have been achieved with reasonably accuracy.
Gyan Wrat, Mohit Bhola, Prabhat Ranjan, Santosh Kr Mishra, and J. Das
Elsevier BV
Prabhat Ranjan, Gyan Wrat, Mohit Bhola, Santosh Kr. Mishra, and J. Das
Elsevier BV
Hera Shaheen, Shikha Agarwal, and Prabhat Ranjan
Springer Singapore
Gyan Wrat, Prabhat Ranjan, Mohit Bhola, Santosh Kumar Mishra, and J Das
SAGE Publications
The role of hydraulic systems is quite evident especially in the case of heavy machineries employed in industries, where the utilisation of high forces amid large stiffness is the prerequisite. Nevertheless, there has been substantial effort put forward in the development of advanced control strategies which finally addressed the issue of the position control. Proportional–integral–derivative control strategy happens to be one among them, which is a versatile and widely renowned approach involved in the position control in this study. Although, it is quite frequently observed that the hydraulic actuation system possesses strong nonlinearities. In this article, two different actuator position control strategies, that is, swash plate control of main pump and speed control strategy of prime mover are compared. In swash plate control strategy, the proportional–integral–derivative controller adjusts the swash plate of main pump through servo mechanism, whereas in the speed control strategy, the proportional–integral–derivative controller adjusts the speed of the electric motor through variable-frequency drive. For this purpose, two MATLAB-Simulink models are developed and validated experimentally. It is found that swash plate control strategy has better dynamic and control performance than the speed control strategy catering same position demand of the linear actuator.
Sunny Kumar, Praveen Kumar, Archisman Barat, Ashutosh K. Sinha, P. Parth Sarthi, Prabhat Ranjan, and K. K. Singh
Springer Science and Business Media LLC
Shikha Agarwal, Akshay Dhyani, and Prabhat Ranjan
IOS Press
Shikha Agarwal and Prabhat Ranjan
Inderscience Publishers
Particle swarm optimisation (PSO) is a popular nature inspired computing method due to its fast and accurate performance, exploration and exploitation capability, cognitive and social behaviour and has fewer parameters to adjust. Recently, an improved binary PSO (IBPSO) was proposed by Chuang et al. (2008) to avoid getting trapped in local optimum and they have shown that it outperforms all other variants of PSO. Even though many variants of PSO exists independently to improve the performance of PSO, to escape from local optimum and to deal with dimensionality reduction, there still needs an integrated approach to handle it. Hence, in this paper, two tiers PSO architecture (TTPA) is proposed to find the maximum classification accuracy with minimum number of selected features. The proposed method is used to classify nine benchmarking gene expression datasets. The results show the merits of TTPA.
Sushant Kumar and Prabhat Ranjan
Springer Singapore
Santosh Kr. Mishra, Gyan Wrat, Prabhat Ranjan, and J. Das
Springer Science and Business Media LLC
Shikha Agarwal and Prabhat Ranjan
Springer Science and Business Media LLC
Shikha Agarwal and Prabhat Ranjan
Springer Singapore
Amit Kumar and Prabhat Ranjan
Springer Singapore
Shikha Agarwal, R. Rajesh, and Prabhat Ranjan
Springer Science and Business Media LLC
Gautam Kumar, P. Parth Sarth, Prabhat Ranjan, and Sushant Kumar
IEEE
Particle swarm optimization (PSO) is a population based optimization technique, inspired by social behavior of animal and birds, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a brief overview of the basic concepts of clustering techniques proposed in last four decades and a quick review of different similarity measure has been done. K-means is implemented to cluster satellite image of city Mumbai (India) and standard image such as mandrill and clown in HSV color space. PSO is used to optimize clusters results from k-means and within-cluster sums of point-to-centroid distances are measured. The results illustrate that our approach can produce more compact and optimized clusters than the K means alone.
Ashish Ranjan and Prabhat Ranjan
IEEE
Data outbreak following the flourishing of technologies like cloud computing, social networks, and many more stimulate emergence of new paradigm called “Big Data”, which not only improved the decision-making, but also provided a means to easy privacy violation of an individual. Existing data anonymization techniques adhering to certain privacy model such as k-anonymity or l-diversity proved their worth, but demands identifying potential quasi-identifier set manually by the domain expert which is very much prone to human error. The paper focus on the problems of choosing appropriate quasi-identifier set and minimizing information loss due to anonymization process. The proposed approach is two phase entropy driven approach to find the quasi-identifier set in a first phase, which is extended to second phase to anonymize the dataset based on selected quasi-identifier set. Our approach ensures that dataset maintains its statistical properties along with minimal information loss and better privacy.
Shikha Agarwal and Prabhat Ranjan
IOS Press
Abdullah Imran and Prabhat Ranjan
Springer Singapore
Dhananjay Kumar, P. Parth Sarthi, and Prabhat Ranjan
IEEE
Rainfall and corresponding Runoff estimation are substantially dependent on various geographic, climatic, and biotic features of the catchment or basin under study and these factors often induce a linear, non-linear or highly complex relation between rainfall and runoff. The few of key factors include precipitation, percolation, infiltration, evaporation, stream flow, and air temperature. Plenty of Rainfall-Runoff (RR) regression models are available, each one distinguished by a varying level of complexity and data requirement. Most of the time due to complex relationship between rainfall and runoff the traditional models (SCN-CN, MISDc, GA, CN4GA) with regression equations don't resembles the correct scene of rainfall-runoff connection. Computational Intelligence (CI) approaches play a key role in modeling those complex tie-ups between rainfall and runoff. The rainfall-runoff process was modeled using a mamdani Fuzzy Inference System (FIS) implemented within a layered design of Artificial Neural Network (ANN) and was applied to a small area of Koshi basin in Bihar, using 12 year's (1980-1992) observed records of daily rainfall, soil moisture and runoff. A comparison was also made between proposed models and existing soft computing models. The proposed computational intelligence model proves significantly better than existing soft computing models in terms of performance.
Gautam Kumar, P. Parth Sarthi, Prabhat Ranjan, and R. Rajesh
IEEE
This paper throws a light on the available clustering techniques and algorithms, k-means is used to cluster standard and satellite image in RGB and HSV color space. Normally satellite images comes with data and noises, in order to extract meaningful information efficiently there is a need of image clustering and performance of clustering based on pixel classification is greatly affected by the color space we selected, because image analysis in terms of Red, Green and Blue components is more difficult as compared to in terms of hue, saturation and value in context of differentiation an object. Our analysis of image clustering in two different color spaces using the k-means technique shows that clustering performance decreases with RGB color space when compared to HSV color space. CHI, DBI and SE indexes are calculated and compared.