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The inhibition effect of propargyl alcohol on the acidic corrosion of mild steel in 5 per cent commercial hydrochloric acid was studied at different temperatures by mass loss measurements and polarization studies. Corrosion potential and corrosion current have been calculated in the presence and in the absence of inhibitor. The inhibitor efficiency increases with increase in concentration and it decreases with rise in temperature. The interrelationship between the surface coverage and concentration of the inhibitor was also studied. It has been found that the inhibitor obeys Temkin adsorption isotherm.

The purpose of this paper is to incorporate prior knowledge in the artificial neural network (ANN) model for the prediction of continuous cooling transformation (CCT) diagram of steel, so that the model predictions become valid from materials engineering point of view.

Genetic algorithm (GA) is used in different ways for incorporating system knowledge during training the ANN. In case of training, the ANN in multi-objective optimization mode, with prediction error minimization as one objective and the system knowledge incorporation as the other, the generated Pareto solutions are different ANN models with better performance in at least one objective. To choose a single model for the prediction of steel transformation, different multi-criteria decision-making (MCDM) concepts are employed. To avoid the problem of choosing a single model from the non-dominated Pareto solutions, the training scheme also converted into a single objective optimization problem.

The prediction results of the models trained in multi and single objective optimization schemes are compared. It is seen that though conversion of the problem to a single objective optimization problem reduces the complexity, the models trained using multi-objective optimization are found to be better for predicting metallurgically justifiable result.

ANN is being used extensively in the complex materials systems like steel. Several works have been done to develop ANN models for the prediction of CCT diagram. But the present work proposes some methods to overcome the inherent problem of data-driven model, and make the prediction viable from the system knowledge.

The purpose of this paper is to help newly established plants with minimal or no historical machine data select best maintenance strategies that suit their specific working setup and at the same time satisfy relevant selection criteria.

Analytical hierarchy process (AHP) was applied successfully in this study to select the maintenance strategy at a newly established chemical fertilizers plant. Implementation started by identifying main and sub-criteria pertinent to maintenance practice in this particular industry. Pair-wise comparisons and consistency calculations were carried out on the chosen criteria and then were used to assess candidate maintenance strategies through a special scoring process. The methodology included the use of surveys, brainstorming and expert consultation.

The results have shown that the most important main criteria are cost, resources, failures, management, operations, quality and safety. The final maintenance strategy selected for the plant under consideration included a mix of condition-based predictive maintenance (PDM), time-based preventive maintenance (PM) and corrective maintenance (CM). The best balance between the three maintenance activities, which satisfies the maintenance criteria with technical applicability, was found to be 50, 23 and 19% for PDM, PM and CM, respectively.

The present paper is a novel application of AHP coupled with deterministic application-specific ranking for devising a procedure for selecting viable and applicable comprehensive maintenance strategies for newly established chemical fertilizers plants with no historical data on machine failures.

Geographical disparities in the light of regional development constitute ever present issue affecting academic debates as well as decision process of policy makers also in the Central and East European countries, mainly during the last two decades. The purpose of this paper is to outline the economic development of one of the most underdeveloped regions in Slovakia, of Banská Bystrica, during the transformation stage of post-socialist societal development, with emphasis on the period after 2000, in the context of the economic performance related to other regions of the country.

For this purpose, several economic indicators (unemployment rate, vacancies, employment in economic sectors, wages, gross domestic product, foreign direct investment and housing construction) are utilised, whose common contribution to assessing the economic performance of a territorial system is secured by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodological approach. Thus, analytical part of the study stems from standard statistical data, enriched by 11 in-depth interviews conducted with stakeholders involved in socio-economic and political life of the region.

Of internal factors, innovation capacity of the region and supporting the business environment appear to be a key for its further economic development. Attractiveness for foreign direct investment as well as social cohesion of the EU are considered the crucial factors of regional development stemming from the external environment.

Using TOPSIS method and series of in-depth interviews with regional stakeholders the authors identified development prospects of underdeveloped Banská Bystrica region, in the context of opportunities and threats forming its presence in the near future.

Acidization is one of the most important techniques to increase the productivity of crude petroleum oil and gas wells. The effective way for protecting these oil well tubular materials is by using corrosion inhibitors during the acidization process. The present study deals with the evaluation of inhibition effect of ethanolamines on oil well tubular materials of grade N‐80 steel in 15 per cent hydrochloric acid at room temperature with 0‐2 per cent amine concentrations. The amines such as mono, di and triethanolamine were studied for their inhibiting capacity by mass loss method, DC polarization method and AC Impedance method. The inhibitor efficiency increases with increasing concentration of amines. Monoethanolamine is found to be more effective than the other two amines.

Prefabricated construction concerns off-site production, multi-mode transportation and on-site installation of the prefabricated components, which are interdependent and dynamically interactive, so coordination among the multiple stages along the prefabricated component supply chain (PCSC) is indispensable. This study aims to solve the dynamic transportation planning problem for the PCSC by addressing the interdependency, dynamic interaction and coordination among the multiple stages and different objectives of the stakeholders.

The PCSC is analyzed and then the formulation for the dynamic transportation planning problem is developed based on the just-in-time (JIT) strategy. The particle swarm optimization (PSO) algorithm is applied to solve the dynamic optimization problem.

The proposed dynamic transportation planning method for the PCSC regarding component supplier selection, transportation planning for means, routes and schedule, site layout planning and transportation plan adjustment is able to facilitate coordination among the multiple stages by addressing their interdependencies and dynamic interactions, as well as different economic objectives of the stakeholders such as suppliers or the contractor.

The study helps to achieve the advantages of prefabricated construction by prompting coordination among multiple stages of the PCSC by realizing different benefits of the stakeholders. In addition, it provides the stakeholders with the competitive bidding prices and the evaluation data for the bids quote. Meanwhile, it contributes to the domain knowledge of the PCSC management with regard to the viewpoint of coordination and integration of multiple stages rather than only one stage as well as the dynamic optimization model based on the JIT strategy and the PSO algorithm.

The aim of this paper is to synthesize knowledge related to performance evaluation of automated construction processes during the planning and execution phases through a theme-based literature classification. The primary research question that is addressed is “How to quantify the performance improvement in automated construction processes?”

A systematic literature review of papers on automated construction was conducted involving three stages-planning, conducting and reporting. In the planning stage, the purpose of the review is established through key research questions. Then, a four-step process is employed consisting of identification, screening, shortlisting and inclusion of papers. For reporting, observations were critically analysed and categorized according to themes.

The primary conclusion from this study is that the effectiveness of construction processes can only be benchmarked using realistic simulations. Simulations help to pinpoint the root causes of success or failure of projects that are either already completed or under execution. In automated construction, there are many complex interactions between humans and machines; therefore, detailed simulation models are needed for accurate predictions. One key requirement for simulation is the calibration of the models using real data from construction sites.

This study is based on a review of 169 papers from a database of peer-reviewed journals, within a time span of 50 years.

Gap in research in the area of performance evaluation of automated construction is brought out. The importance of simulation models calibrated with on-site data within a methodology for performance evaluation is highlighted.

Construction progress monitoring (CPM) is considered a difficult and tedious task in construction projects, which focuses on identifying discrepancies between the as-built product and the as-planned design. Computer vision (CV) technology is applied to automate the CPM process. However, the synergy between the CV and CPM in literature and industry practice is lacking. This study aims to fulfil this research gap.

A Delphi qualitative approach was used in this study by conducting two interview rounds. The collected data was analysed using manual content analysis.

This study identified seven stages of CPM; data acquisition, information retrieval, verification, progress estimation and comparison, visualisation of the results and schedule updating. Factors such as higher accuracy in data, less labourious process, efficiency and near real-time access are some of the significant enablers in instigating CV for CPM. Major challenges identified were occlusions and lighting issues in the site images and lack of support from the management. The challenges can be easily overcome by implementing suitable strategies such as familiarisation of the workforce with CV technology and application of CV research for the construction industry to grow with the technology in line with other industries.

This study addresses the gap pertaining to the synergy between the CV in CPM literature and the industry practice. This research contributes by enabling the construction personnel to identify the shortcomings and the opportunities to apply automated technologies concerning each stage in the progress monitoring process.

The purpose of this paper, an experimental study, is to investigate the optimal machining parameters for turning of nickel-based superalloy Inconel 718 under eco-friendly nanofluid minimum quantity lubrication (NMQL) environment to minimize cutting tool flank wear (Vb) and machined surface roughness (Ra).

The central composite rotatable design approach under response surface methodology (RSM) is adopted to prepare a design of experiments plan for conducting turning experiments.

The optimum value of input turning parameters: cutting speed (A), feed rate (B) and depth of cut (C) is found as 79.88 m/min, 0.1 mm/rev and 0.2 mm, respectively, with optimal output response parameters: Vb = 138.633 µm and Ra = 0.462 µm at the desirability level of 0.766. Feed rate: B and cutting speed: A2 are the leading model variables affecting Vb, with a percentage contribution rate of 12.06% and 43.69%, respectively, while cutting speed: A and feed rate: B are the significant factors for Ra, having a percentage contribution of 38.25% and 18.03%, respectively. Results of validation experiments confirm that the error between RSM predicted and experimental observed values for Vb and Ra is 3.28% and 3.75%, respectively, which is less than 5%, thus validating that the formed RSM models have a high degree of conformity with the obtained experimental results.

The outcomes of this research can be used as a reference machining database for various metal cutting industries to establish eco-friendly NMQL practices during the turning of superalloy Inconel 718 to enhance cutting tool performance and machined surface integrity.

No study has been communicated till now on the turning of Inconel 718 under NMQL conditions using olive oil blended with multi-walled carbon nanotubes-based nanofluid.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0317/