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Steel, stainless steel, clad steel, and aluminium are used in the transport and handling of chlorosulphonic acid, which is used as a sulphonating agent in the manufacture of synthetic drugs and as a warfare chemical. The choice of the appropriate material of construction for tank wagons and handling equipment depends upon the rate of corrosion of these materials in chlorosulphonic acid. Experiments show that corrosion can vary widely depending upon the percentage of chlorosulphonic acid present.

This paper aims to optimally plan distributed generation (DG) and capacitor in distribution network by optimizing multiple conflicting operational objectives simultaneously so as to achieve enhanced operation of distribution system. The multi-objective optimization problem comprises three important objective functions such as minimization of total active power loss (P^loss_total), reduction of voltage deviation and balancing of current through feeder sections.

In this study, a hybrid configuration of weight improved particle swarm optimization (WIPSO) and gravitational search algorithm (GSA) called hybrid WIPSO-GSA algorithm is proposed in multi-objective problem domain. To solve multi-objective optimization problem, the proposed hybrid WIPSO-GSA algorithm is integrated with two components. The first component is fixed-sized archive that is responsible for storing a set of non-dominated pareto optimal solutions and the second component is a leader selection strategy that helps to update and identify the best compromised solution from the archive.

The proposed methodology is tested on standard 33-bus and Indian 85-bus distribution systems. The results attained using proposed multi-objective hybrid WIPSO-GSA algorithm provides potential technical and economic benefits and its best compromised solution outperforms other commonly used multi-objective techniques, thereby making it highly suitable for solving multi-objective problems.

A novel multi-objective hybrid WIPSO-GSA algorithm is proposed for optimal DG and capacitor planning in radial distribution network. The results demonstrate the usefulness of the proposed technique in improved distribution system planning and operation and also in achieving better optimized results than other existing multi-objective optimization techniques.

Economic load dispatch (ELD) is one of the crucial optimization problems in power system planning and operation. The ELD problem with valve point loading (VPL) and multi-fuel options (MFO) is defined as a non-smooth and non-convex optimization problem with equality and inequality constraints, which obliges an efficient heuristic strategy to be addressed. The purpose of this study is to present a new and powerful heuristic optimization technique (HOT) named as squirrel search algorithm (SSA) to solve non-convex ELD problems of large-scale power plants.

The suggested SSA approach is aimed to minimize the total fuel cost consumption of power plant considering their generation values as decision variables while satisfying the problem constraints. It confers a solution to the ELD issue by anchoring with foraging behavior of squirrels based on the dynamic jumping and gliding strategies. Furthermore, a heuristic approach and selection rules are used in SSA to handle the constraints appropriately.

Empirical results authenticate the superior performance of SSA technique by validating on four different large-scale systems. Comparing SSA with other HOTs, numerical results depict its proficiencies with high-qualitative solution and by its excellent computational efficiency to solve the ELD problems with non-smooth fuel cost function addressing the VPL and MFO. Moreover, the non-parametric tests prove the robustness and efficacy of the suggested SSA and demonstrate that it can be used as a competent optimizer for solving the real-world large-scale non-convex ELD problems.

This study has compared various HOTs to determine optimal generation scheduling for large-scale ELD problems. Consequently, its comparative analysis will be beneficial to power engineers for accurate generation planning.

To the best of the authors’ knowledge, this manuscript is the first research work of using SSA approach for solving ELD problems. Consequently, the solution to this problem configures the key contribution of this paper.

Social media enables sport organizations to connect with customers in a dynamic, ubiquitous, and timely manner. Although these organizations routinely use social media, the best practices to improve customer engagement remain elusive. This paper aims to examine National Football League (NFL) teams’ Instagram posts to understand how sport teams can utilize social media to drive customer engagement.

Guided by uses and gratification theory, the author employs a machine learning algorithm to assess the content of NFL teams’ posts from the 2013–2014 season to the 2017–2018 season. The author performs regression analyses to investigate how post topic, together with confounding factors, boost customer engagement.

Results highlight the importance of informational content in eliciting engagement and reveal distinctions in topics deemed “social content” in the literature. The author further identifies variations in how post topics engage sport fans and general customers.

Results provide implications for sport organizations to craft social media content for customer engagement.

This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.

A total of 3,286 tweets (registering nearly 1.35 million impressions) published by 10 leading Fintech unicorns in India were extracted using the Twitter API. The Linguistic Inquiry and Word Count (LIWC) dictionary was used to analyse the linguistic characteristics of the shared tweets. Negative Binomial Regression (NBR) was used for testing the hypotheses.

This study finds that using drive words and cognitive language increases consumer engagement with Fintech messages via the central route of information processing. Further, affective words and conversational language drive consumer engagement through the peripheral route of information processing.

The study extends the literature on brand engagement by unveiling the effect of linguistic features used to design social media messages.

The study provides guidance to social media marketers of Fintech brands regarding what content strategies best enhance consumer engagement. The linguistic style to improve online consumer engagement (OCE) is detailed.

The study’s findings contribute to the growing stream of Fintech literature by exploring the role of linguistic style on consumer engagement in social media communication. The study’s findings indicate the relevance of the dual processing mechanism of elaboration likelihood model (ELM) as an explanatory theory for evaluating consumer engagement with messages posted by Fintech brands.

The purpose of this paper is to present the application of an adaptive bacterial foraging (BF) algorithm for the design optimization of an energy efficient induction motor.

The induction motor design problem is formulated as a mixed integer nonlinear optimization problem. A set of nine independent variables is selected, and to make the machine feasible and practically acceptable, six constraints are imposed on the design. Two different objective functions are considered, namely, the annual active material cost, and the sum of the annual active material cost, annual cost of the active power loss of the motor and annual energy cost required to supply such power loss. A new adaptive BF algorithm is used for solving the optimization problem. A generic penalty function method, which does not require any penalty coefficient, is employed for constraint handling.

The adaptive BF algorithm is validated for two sample motors and benchmarked with the genetic algorithm, particle swarm optimization, simple BF algorithm, and conventional design methods. The results show that the proposed algorithm outperforms the other methods in both the solution quality and convergence rate. The annual cost of the induction motor is remarkably reduced when designed on the basis of minimizing its annual total cost, instead of minimizing its material cost only.

To the best of the knowledge, none of the existing work has applied the BF algorithms for electrical machine design problems. Therefore, the solution to this problem constitutes the main contribution of the paper. According to the huge number of induction motors operating all over the world, the BF techniques used in their design, on minimum annual cost basis, will lead to a tremendous saving in global energy consumption.

Currently, more renewable energy resources with advanced technology levels are incorporated in the electric power networks. Under this circumstance, the attainment of optimal economic dispatch is very much essential by the power system as the system requires more power generation cost and also has a great demand for electrical energy. Therefore, one of the primary difficulties in the power system is lowering the cost of power generation, which includes both economic and environmental costs. This study/paper aims to introduce a meta-heuristic algorithm, which offers an solution to the combined economic and emission dispatch (CEED).

A novel algorithm termed Levy-based glowworm swarm optimization (LGSO) is proposed in this work, and it provides an excellent solution to the combined economic and emission dispatch (CEED) difficulties by specifying the generation of the optimal renewable energy systems (RES). Moreover, in hybrid renewable energy systems, the proposed scheme is extended by connecting the wind turbine because the thermal power plant could not control the aforementioned costs. In terms of economic cost, emission cost and transmission loss, the suggested CEED model outperforms other conventional schemes genetic algorithm, Grey wolf optimization, whale optimization algorithm (WOA), dragonfly algorithm (DA) and glowworm swarm optimization (GSO) and demonstrates its efficiency.

According to the results, the suggested model for Iteration 20 was outperformed GSO, DA and WOA by 23.46%, 97.33% and 93.33%, respectively. For Iteration 40, the proposed LGSO was 60%, 99.73% and 97.06% better than GSO, DA and WOA methods, respectively. The proposed model for Iteration 60 was 71.50% better than GSO, 96.56% better than DA and 95.25% better than WOA. As a result, the proposed LGSO was shown to be superior to other existing techniques with respect to the least cost and loss.

This research introduces the latest optimization algorithm known as LGSO to provide an excellent solution to the CEED difficulties by specifying the generation of the optimal RES. To the best of the authors’ knowledge, this is the first work that utilizes LGSO-based optimization for providing an excellent solution to the CEED difficulties by specifying the generation of the optimal RES.

The use of public–private partnership (PPP) approaches for developing infrastructure has been well recognized. The allocation of risk between public authority and private sector differs among the different types of PPP projects. The objective of the paper is to analyze the factors that influence the type of PPP and the performance of different types of PPP contracts.

A unique data set of 202 national highway PPP projects from India, comprising 154 toll and 48 annuity projects formed the basis of the study.

There are significant differences between toll and annuity PPP projects. The former are longer, are implemented in better developed states but are also characterized by higher cost over-runs. The latter are characterized by higher debt–equity ratio.

Mitigating revenue risk can significantly enhance the debt capacity of the projects, thereby reducing the overall cost of capital. To make toll roads attractive for bidders, they have to be developed as longer stretches. Toll projects that are immediately ready for development at the time of award would reduce cost overruns of toll projects and sustain the interest of private developers.

Comparison of toll and annuity PPP road projects has never been done previously. The unique data set used in this study highlights the differences in characterization and performance for both the project types. The study provides evidence support to “intuition” and enables policymakers to choose the right form of PPP to realize their objectives.

The primary objective of this investigation is to optimize the constricted arc tungsten inert gas (CA-TIG) welding parameters specifically welding current (WC), arc constriction current (ACC), ACC frequency (ACCF) and CA traverse speed to maximize the tensile properties of thin Inconel 718 sheets (2 mm thick) using a statistical technique of response surface methodology and desirability function for gas turbine engine applications.

The four factor – five level central composite design (4 × 5 – CCD) matrix pertaining to the minimum number of experiments was chosen in this investigation for designing the experimental matrix. The techniques of numerical and graphical optimization were used to find the optimal conditions of CA-TIG welding parameters.

The thin sheets of Inconel 718 (2 mm thick) can be welded successfully using CA-TIG welding process without any defects. The joints welded using optimized conditions of CA-TIG welding parameters showed maximum of 99.20%, 94.45% and 73.5% of base metal tensile strength, yield strength and elongation.

The joints made using optimized CA-TIG welding parameters disclosed 99.20% joint efficiency which is comparatively 20%–30% superior than conventional TIG welding process and comparable to costly electron beam welding and laser beam welding processes. The parametric mathematical equations were designed to predict the tensile properties of Inconel 718 joints accurately with a confidence level of 95% and less than 4.5% error. The mathematical relationships were also developed to predict the tensile properties of joints from the grain size (secondary dendritic arm spacing-SDAS) of fusion zone microstructure.

The purpose of this paper is to solve the realistic problem of source maintenance scheduling (SMS) based on reliability criterion. A novel effective optimization technique is proposed to solve the problem at hand.

The problem has been formulated as a combinatorial optimization task, with the goal of maximizing reliability by minimizing the sum of squares of the reserve loads while satisfying unit and system constraints. This paper employs a nature inspired algorithm known as Teaching Learning Based Optimization (TLBO) for solving the SMS problem based on reliability.

The results reveal that optimal maintenance schedules of generating units has been obtained using TLBO algorithm with minimized values of sum of squares of reserve loads while satisfying system and operational constraints. It is also found that the inclusion of resource constraints (RC) in the model have significant effects on the objective function value which provides a deep insight of the proposed methodology.

The contribution of this paper is that an efficient nature inspired algorithm has been applied to solve source maintenance scheduling problem in viewpoint of the planning for future system capacity expansion. The incorporation of exclusion and RC in the model makes the analysis about the impact of SMS on the system reliability more reasonable.