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Over the year, garbage from industry establishments’ everyday operations included several different sorts. For instance, among the most prevalent industrial wastes formed in India is sugarcane bagasse ash (SCBA). This study’s main goal is to examine the possibilities for using SCBA in burnt clay bricks. Using mechanical and thermal characteristics, the impact of SCBA and ruthenium (Ru) nanoparticle additions to the brick production clay has been studied. To achieve this, combinations of brick-making clay containing 15 wt% SCBA and different weight portions of Ru nanoparticles (by 0, 1, 3, 5, and 7%) were hydraulically uniaxially squeezed and sintered at 1000 ℃. Compressive strength, thermogravimetric analysis (TGA), and a water absorption test were among the mechanical and thermal parameters that were assessed and contrasted. Using a scanning electron microscope, the microstructure of the specimen’s interfacial layer was also investigated. Additionally, Fourier-transform infrared spectroscopy is used to examine the novel nano clay brick’s chemical structure. A clay brick with up to 5 wt% Ru nanoparticles has greater compressive strength (32.6 MPa maximum) and thermal strength (8.8% wt loss) than a brick with 0 wt% (24.8 MPa and 31% wt loss), according to experimental results. However, the compressive and thermal strengths were decreased for 7% Ru nanoparticles brick (30.2 MPa and 9.1% wt loss).

In this research work, the effect of minimum quantity lubrication (MQL) parameters on the material removal rate and surface roughness of AISI-4320 steel using biochar nanofluid in turning process was studied. The main aim of this present work was to optimize the process parameters of MQL turning process using Taguchi method. The turning process outputs such as material removal rate and surface roughness were measured and analyzed using gray relational grades to determine the process efficiency using biochar nanofluid. The study was conducted using Taguchi L9 orthogonal array followed by grey relational method. According to the analysis, the nanofluid concentration in minimum quantity lubrication was the most influencing process parameter with the lowest fluid flow rate to produce the highest material removal rate of 58.312 mm3/s and low surface roughness of 3.236 μm. The machining process parameter levels such as A3B1C3are found to be the promising process variable levels, where the highest material removal rate and lower surface roughness were produced. The SEM micrographs of the tool tip show a lower tool wear for machining with nanofluid of 2 wt.% biochar.

This paper proposes the particle swarm optimization (PSO) technique-based proportional integral derivative (PID) controller suggested for frequency regulation of a micro grid (MG) system. MG system integrates with thermal power generating units, renewable energy sources (RES) like photovoltaic (PV), wind energy generators (WTG), and Energy storage systems (ESS) such as fuel cell (FC) and battery energy storage system (BESS). Indentifying the supremacy of proposed technique-based controller and supremacy is examined with three objective functions (integral absolute error [IAE], integral time absolute error [ITAE], and integral squared error [ISE]). The results of the system are compared with conventional PID controller results. From the comparison, it is clearly evident that PSO-PID controller gives better performance over conventional methods in terms of various time domain specific parameters such as settling time, peak overshoot, and undershoot. In both methods, ITAE objective function used controller produce more effective response in MG under sudden load demand situation.

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control, automatic generation control &#x0028 AGC &#x0029 plays a crucial role. In this paper, multi-area &#x0028 Five areas: area 1, area 2, area 3, area 4 and area 5 &#x0029 reheat thermal power systems are considered with proportional-integral-derivative &#x0028 PID &#x0029 controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm &#x0028 FFA &#x0029. The experimental results demonstrated the comparison of the proposed system performance &#x0028 FFA-PID &#x0029 with optimized PID controller based genetic algorithm &#x0028 GA-PID &#x0029 and particle swarm optimization &#x0028 PSO &#x0029 technique &#x0028 PSO-PID &#x0029 for the same investigated power system. The results proved the efficiency of employing the integral time absolute error &#x0028 ITAE &#x0029 cost function with one percent step load perturbation &#x0028 1 &#x0025 SLP &#x0029 in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot &#x002F undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.

In this article, the optimal gain value of classical controller gain values is obtained by using different performance indices in Automatic Generation Control. These are interconnected by the three areas of a hydro-thermal power system. The thermal and hydro areas are incorporated with a reheat turbine and a mechanical governor, respectively. The current article was created to select a suitable technique for the tuning of controller gain, when 1% of a step load is given to the thermal area (Area 1). The performance of several controllers, such as Integral (I), Proportional-Integral (PI), and Integral-Derivative (ID) were evaluated and were compared to the cases of with and without Generation Rate Constraint (GRC) non-linearity. The classical controller gain value optimization was performed using the Integral Time Square Error (ITSE), Integral Square Error (ISE) and Integral Time Absolute Error (ITAE) performance indices. The minimum optimal value of controller gain normally offers better dynamic response. The obtained results established that the optimization based on the ITAE-PI controller always guarantees superior dynamic performances compared to other indices and controllers.

The current study presents the load frequency control (LFC) of multi-area reheat thermal power system with proportional-integral-derivative (PID) controller. The interconnected control areas are provided with a single stage reheat turbine in all areas. The proportional gain (KP), integral gain (KI) and derivative gain (KD) values of the PID controller are simultaneously optimised using recent and powerful evolutionary computational intelligence technique, namely the particle swarm optimisation (PSO) algorithm. The superiority of the PSO-based PID controller has been proved by comparing its performance to recent modern optimisation techniques such as hill climbing (HC) algorithm and genetic algorithm (GA) tuned controllers for the same multi-area thermal power system. For the analysis, the time domain specification and 1% step load perturbation (1% SLP) are considered in thermal area 1. The simulation result showed that the proposed PSO-based PID controller provides superior dynamic response over other optimisation technique (HC and GA)-based PID controller.