Mechanical engineering plays an important role in the design and manufacture of medical devices, implants, prostheses, and other medical equipment, where the machining of bio-compatible materials have a special place. There are a lot of different conventional and non-conventional types of machining of biocompatible materials. One of the most frequently used methods is milling. The first part of this research explores the machining parameters optimization minimizing surface roughness in milling titanium alloy Ti-6Al-4V. A full factorial design involving four factors (cutting speed, feed rate, depth of cut, and the cooling/lubricating method), each having three levels, implies the 81 experimental runs. Using the Taguchi method, the number of experimental runs was reduced from 81 to 27 through an orthogonal design. According to the analysis of variance (ANOVA), the most significant parameter for surface roughness is feed rate. The second part explores the possibilities of using different ML techniques to create a predictive model for average surface roughness using the previously created small datasets. The paper presents a comparative analysis of several commonly used techniques for handling small datasets and regression problems. The best results indicate that the widely used machine learning algorithm Random Forest excels in handling regression problems and small datasets.

Mechanical engineering plays an important role in the design and manufacture of medical devices, implants, prostheses, and other medical equipment, where the machining of bio-compatible materials have a special place. There are a lot of different conventional and non-conventional types of machining of biocompatible materials. One of the most frequently used methods is milling. The first part of this paper considers the optimization of machining parameters for minimizing surface roughness in milling biocompatible alloy Ti-6Al-4V. With four factors (cutting speed, feed rate, depth of cut, and cooling/lubricating method), each having three levels, the full factorial design implies 81 experiments have to be carried out. Using the Taguchi method, the experiment number was reduced from 81 to 27 runs through an orthogonal design. According to the analysis of variance (ANOVA), the most significant parameter for surface roughness is feed rate. In the second part possibilities of different ML techniques to create a predictive model for average surface roughness using previously created small dataset are explored. Several ML technologies and techniques that can deal successfully with small dataset were explored. The best results show commonly used machine learning algorithm Random Forest that is widely used in regression problems.

Water networks are critical infrastructure components, ensuring the continuous supply of high-quality drinking water to consumers. To secure such water supply, regular maintenance, including the replacement of deteriorating pipelines, is essential. In this study, a methodology has been developed for determining optimal pipeline replacement solutions in water supply systems at water utilities with limited data availability. Hydraulic analysis has been conducted on the segment of 25 km of the water supply network using the free software EPANET (Environmental Protection Agency, NETwork) Applying water network optimization, eight pipeline replacement projects according to 13 pre-defined criteria have been identified and evaluated. The paper outlines the methods for evaluating the criteria, including defining specific quantitative limits. The Analytical Hierarchical Process (AHP) method was used in the paper to determine the weights of the criteria. The reason for applying this method refers to problems that involve a set of criteria with a mixed structure, including both quantitative and qualitative aspects. Also, the paper describes the steps of the multi-criteria optimization method VIKOR (Serbian language – VIšekriterijumsko KOmpromisno Rangiranje), used to select the optimal project. The obtained results were also confirmed by the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) multi-criteria optimization method. This paper, considered as a case study, describes a method, i.e., application of a new principle and an innovative way to solve a problem for developing countries.

The application of circular economy principles in water supply systems has been increasingly studied recently. Concrete experiments and practical implementations in the industry are being carried out with the aim of cost savings, energy production, and material and energy recovery from wastewater treatment processes. In this context, the aim of this paper is to identify some measures that water utilities can take to enhance sustainability and reduce environmental impact. The paper provides a brief overview of the opportunities for water utilities to act on water, materials, and energy.

This paper analyzes the state of water resources and municipal infrastructure in the context of the Environmental Protection Strategy of the Republic of Srpska (2022-2032). Although the legal framework has been largely harmonized with the directives of the European Union, sectoral analyses indicate significant infrastructure challenges. In particular, high water losses in distribution networks (an average of 42.6%) and a pronounced imbalance between water supply coverage (60%) and sewerage network coverage (36%) are highlighted. The paper presents strategic measures and priorities, including the zoning of water distribution networks, the construction of wastewater treatment plants, and the implementation of green infrastructure to support adaptation to climate change.

Paper analyzes energy consumption and optimization potentials in wastewater treatment plants (WWTPs). The paper focuses on identifying key factors affecting energy use across different treatment stages, including preliminary, primary, secondary, and tertiary treatment. Strategies for improving energy efficiency are also discussed, such as the implementation of advanced technologies, aeration optimization, real-time process control, and the utilization of by-products, especially sludge, for biogas production. Based on literature review and case studies, the paper highlights the potential for transforming WWTPs into energy self-sufficient systems, reducing operational costs and greenhouse gas emissions while maintaining the quality of treated water.