The Scientific Journal of Riga Technical University
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Sound Insertion Loss Performance of Baffles with Devulcanized Waste Rubber
Full text linkIn this study, the insertion loss of devulcanized waste rubber baffles was evaluated. Acoustic baffles are suitable for reducing noise from the devices or machines by interfering with their emitting sound waves. Knowledge of the acoustic properties of the material used is of significant importance in ensuring the effectiveness of the acoustic properties of the baffle. Basic properties include airborne sound insulation, which is usually determined during laboratory testing. Baffle consists of sound absorbing and sound insulating materials. In this study, plasterboards were used as sound insulating material and devulcanized waste rubber as sound absorbing material. Devulcanization targets mostly the scission of sulphur crosslinks. Devulcanization techniques that have been explored in rubber recycling include thermos-chemical, microbiological, ultrasonic microwave thermos-chemical devulcanization in a supercritical carbon dioxide medium (scCO2). In this study, two types of rubber granules were devulcanized by grinding method and one other type was chemically devulcanized. Three types of rubber granules were mixed together in increasing 25 % proportion steps and glued with patented polyurethane glue. Total of 15 different composition devulcanized waste rubber granule boards were made. Rubber boards were attached together with the plasterboards. Insertion loss of the different composite baffles was measured in semi-anechoic chamber in a purposefully designed stand in 1/3rd-octave bands. The results showed that the insertion loss of the baffles depends mostly on the rubber granule board density. Increasing the density of the rubber board, insertion loss also increased. 5–6 dB insertion loss difference was measured between the most and the least dense rubber granule board baffles
Heating System Control with Neural Network
Full text linkBuilding performance has significant impact on humans’ life and ecology as buildings account for 39% of total greenhouse gas emissions and consume about 40% of total global energy consumption. Smart building control is one of the key points to archive high energy efficiency. Each year, the complexity of building state control grows due to the increase in the number of controlled elements that are used to achieve better indoor climate. Therefore, in the manual analysis and implementation of the building control program, an error can easily appear due to the human factor. Artificial intelligence (AI) algorithms could be used as an alternative solution as they could evaluate building dynamics independently. One of strategies for automatic building control adaptation to its dynamic is model based predictive control where neural network is used for different control strategies evaluation. Performance of such control technique is highly dependent on control strategies evaluation accuracy. To achieve top accuracy, several hyperparameters of neural network could be tuned as well as data set for specific construction must be prepared. Preparation of data set could be a problem as random control of building for generation of dataset could be not unacceptable for building users as well as it could damage construction.In this paper authors process optimization of experimental building heating system control algorithm to achieve smaller fluctuations of temperature indoors. For dataset generation were used several data from weather station as well as heating system parameters and temperature indoors while building was controlled by thermostat with build in PID regulation. For evaluation of building dynamics were used temporal convolutional neural network. To achieve high accuracy results of control strategies evaluation, several hyperparameters of neural network were tested. Finally resulting model were tested on physical building. Results indicate that in some cases developed control model could prevent temperature fluctuations which could be caused by limits of heating system power
Optimized Centroid-Based Clustering of Dense Nearly-square Point Clouds by the Hexagonal Pattern
No full textAn approach to optimize centroid-based clustering of flat objects is suggested, which is practically important for efficiently solving metric facility location problems. In such problems, the task is to find the best warehouse locations to optimally service a given set of consumers. An example is assigning mobiles to base stations of a wireless communication network. We suggest a hexagonal-pattern-based approach to partition flat nodes into clusters quicker than the k-means algorithm and its modifications do. First, a hexagonal cell lattice is applied to nodes to approximately determine centroids of the clusters. Then the centroids are used as initial centroids to start the k-means algorithm. The suggested method is efficient for centroid-based clustering of dense nearly-square point clouds of 0.1 million points and greater by using no fewer than 6 lattice cells along an axis. Compared to k-means, our method is at least 10 % faster and it is about 0.01 to 0.07 % more accurate in regular Euclidean distances. In squared Euclidean distances, the accuracy gain is 0.14 to 0.21 %. Applying a hexagonal cell lattice determines an upper bound of the clustering quality gap
Energy Efficiency Improvement for Manufacturing Companies in Latvia
No full textConcerns about climate change and environmental issues are becoming more and more popular nowadays and are causing concerns in everyone's life and beyond. One of significant causes of climate change is energy consumption, especially in the manufacturing sector. Even though, energy efficiency (EE) topic has been extensively discussed, not all EE aims are met. For the manufacturing sector the reason is that EE is often not a top priority for the companies. Currently the companies are also faced with another big challenge – in this digital era of rapidly developing technologies they have to adjust their practices to seize the opportunities provided by digitalization and automatization. Therefore, the aim of this research is to explore how digitization can be combined with EE to promote the resilience and increase performance of manufacturing companies, and how remote data analysis, also by using machine learning tools, can help improve EE. Literature review, content analysis, empirical data analysis and cases studies from real production companies in Latvia are used to achieve the research goals. The results section shows how digital tools and real-time monitoring help assessing the current state of the business and make decisions on changes to future operations, helping to reduce consumption and the environmental impact of production. Manufacturing companies have the potential to improve EE through digitalisation, but the manufacturing processes are complex and Latvian companies are only slowly moving towards digitalisation. Therefore, there is a strong need to examine empirical cases to gather more perspective and to find a way to implement digitalization and EE improvement across all sectors. Conclusions suggest that manufacturing companies should be encouraged to move towards a more unified energy data collection system, enabling more efficient data analysis and proposals for energy efficiency improvements
Proportioning of Oil Shale Ash for Sustainable 3D Printable Mortars
No full textTo achieve optimal strength and printability, mortars used in 3D printing typically contain high proportions of cement and other fine-grained powders. Consequently, the majority of mixtures used in 3D printing have high carbon footprint. Hence, there arises a critical need to study alternative supplementary cementitious materials aimed at reducing the environmental impact of mortars used in 3D printing. The use of oil shale ash as a partial substitute for cement not only addresses this issue but also presents an opportunity to repurpose waste from power plants in the Baltic states, where oil shale is intensively utilized.In this study, the influence on mechanical properties and durability of cement-based mortars was evaluated by substituting cement with oil shale ash in varying quantities. Specifically, 0% to 40% of cement mass was replaced with oil shale ash. Life cycle assessment (LCA) was performed for each mixture. By analyzing the material properties alongside the environmental impact for each mixture, the optimal percentage of substitute was determined.For the determination of the mechanical properties of each mixture, compressive and flexural strength tests were conducted on 3D printed samples in various directions, as well as on cast samples. To assess the durability of each mixture, freeze-thaw tests were performed on both 3D printed and cast samples.From the obtained results, we developed an algorithm that chooses the optimal mixture proportioning. Depending on material performance requirements set in the beginning, this algorithm gives the exact proportions of oil-shale ash and cement for the mixture, by taking into account both desired material properties and carbon dioxide emissions. As a result, an environmentally friendly cement-based mixture is obtained without losing the desired properties. By using this algorithm, it is possible to create a mortar with properties comparable to concrete with strength class C30/37 while reducing carbon emissions by 15% to 30%
Incorporating Life Cycle Assessment in the Green Metric Ranking: a Conceptual Approach
No full textIntegrating sustainability principles into their daily operations is an overarching goal of universities and higher education institutions (HEIs), as knowledge multipliers, have to perform (Filimonau et al., 2021).Various studies have demonstrated that the operational activities of universities cause GWP emissions and, in general, negative environmental impacts, due mainly to student and staff mobility, on-campus energy and water consumption, and waste production (Jürgens et al., 2023). Findler et al. (2019) outlined numerous analytical methods, ranging from input-output analysis to full Life Cycle Assessment (LCA), for evaluating the carbon footprints of universities and colleges.Concurrently, a variety of tools have been devised to measure sustainability based on environmental metrics, such as the Green Metric (GM) ranking developed by Universitas Indonesia (UI). The GM, which is first in which tops university sustainability rankings (Marrone et al., 2018), rates HEIs by utilizing 51 criteria across 6 rating areas. Researchers analyzed the UI Green Metric World Ranking system to examine the requirements for a fair sustainability ranking of worldwide HEIs, although the latter were only examined in general without inspecting each item separately (Boiocchi et al., 2023).In order to lessen the environmental impacts of HEIs, recognized and robust methods must be used to identify appropriate and effective measures. LCA is a standardized (ISO 14040 and 14044) tool for quantifying and reducing environmental impacts throughout the entire life cycle of a product, service, or organization.This study is focused on understanding how LCA can be integrated into GM, and more specifically, how it can assist in achieving a consistent and structured review of specific indexes such as EC4, EC7, EC8, WR2, and TR1.The analysis was conducted by comparing the items one by one. Such a method was implemented as a results of the authors' specialized background, the scientific literature of interest, and the adoption of a critical thinking approach.The study results emphasize the necessity of incorporating LCA into the environmental sustainability strategies of HEIs. This integration is crucial for developing a robust approach adaptable to various local contexts, enhancing the precision in assessing and improving HEIs' sustainability practices. Such a strategy will align HEIs' operational activities more effectively with sustainable development goals. The application of the conceptual approach to a case study is recommended
Elucidating Stakeholder Prioritization for Sustainable Off-Grid Renewable Electrification using the Fuzzy AHP-GPESTLE Framework: A Comprehensive Analysis
No full textPoverty has been linked to the reality of the world’s developing countries, especially in far-flung rural areas where the lack of energy access plays a significant role in the misery of the poor and disadvantaged people. To achieve universal access to energy, the role of rural electrification was emphasized, and off-grid small-scale electricity generation from renewable sources was expected to be a promising solution. However, in the Philippines, where off-grid island communities are scattered along its archipelago, the deployment of such systems in rural areas is still a challenge among stakeholders due to the consideration of various conflicting factors that may put the potential economic gains and other social and environmental benefits at risk. To better understand the multifaceted nature of off-grid energy system sustainability through the perspective of its stakeholders, the Fuzzy Analytical Hierarchy Process (FAHP) was used to determine their most prioritized factors in determining the viability and sustainability of such systems following the GPESTLE framework. This provides a comprehensive and more relevant approach to performing sustainability analysis by looking into the geographical (G), political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) dimensions of these assemblies. The prioritization of 74 expert stakeholders, coming from the industry, academy, and government institutions, has been elucidated by having them perform pairwise comparisons among the various GPESTLE criteria through a survey. Using FAHP, prioritization or weights were already generated per G-PESTLE criterion and sub-criterion. It was found out that among the three institutions, the industry players have the lowest environmental prioritization and can be increased by developing them with cost-efficient renewable technologies. The availability of technology manufacturers and transportation accessibility has been the main consideration in ensuring the reliability of the system’s operation. Minimizing LCOE and increasing the people’s capacity to pay should also be a priority to secure the project’s financial viability. The presence of a community comprehensive land use plan has also been highly favored among developers, which can allow faster processing of permits on the use of indigenous resources and agricultural lands. With these findings, this framework aims to guide policymakers to properly address the challenges of islands lying low in prioritization due to problems on certain sustainability factors. These insights can be relevant in the drafting of a transitional framework on the renewable electrification of off-grid islands, which were usually left out or minimally given attention in the national electrification plans of governments.Poverty has been linked to the reality of the world’s developing countries, especially in far-flung rural areas where the lack of energy access plays a significant role in the misery of the poor and disadvantaged people. To achieve universal access to energy, the role of rural electrification was emphasized, and off-grid small-scale electricity generation from renewable sources was expected to be a promising solution. However, in the Philippines, where off-grid island communities are scattered along its archipelago, the deployment of such systems in rural areas is still a challenge among stakeholders due to the consideration of various conflicting factors that may put the potential economic gains and other social and environmental benefits at risk. To better understand the multifaceted nature of off-grid energy system sustainability through the perspective of its stakeholders, the Fuzzy Analytical Hierarchy Process (FAHP) was used to determine their most prioritized factors in determining the viability and sustainability of such systems following the GPESTLE framework. This provides a comprehensive and more relevant approach to performing sustainability analysis by looking into the geographical (G), political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) dimensions of these assemblies. The prioritization of 74 expert stakeholders, coming from the industry, academy, and government institutions, has been elucidated by having them perform pairwise comparisons among the various GPESTLE criteria through a survey. Using FAHP, prioritization or weights were already generated per G-PESTLE criterion and sub-criterion. It was found out that among the three institutions, the industry players have the lowest environmental prioritization and can be increased by developing them with cost-efficient renewable technologies. The availability of technology manufacturers and transportation accessibility has been the main consideration in ensuring the reliability of the system’s operation. Minimizing LCOE and increasing the people’s capacity to pay should also be a priority to secure the project’s financial viability. The presence of a community comprehensive land use plan has also been highly favored among developers, which can allow faster processing of permits on the use of indigenous resources and agricultural lands. With these findings, this framework aims to guide policymakers to properly address the challenges of islands lying low in prioritization due to problems on certain sustainability factors. These insights can be relevant in the drafting of a transitional framework on the renewable electrification of off-grid islands, which were usually left out or minimally given attention in the national electrification plans of governments
Chitosan/Graphene oxide/SiO2 Nanoadsorbents for the Removal of Cr(VI) from Wastewaters
No full textThe swift industrialization and urbanisation have led to the discharge of significant amounts of hazardous heavy metals into water environments. Heavy metal pollution is currently one of the most significant environmental challenges being addressed, attracting researchers due to its biotoxicity, and non-biodegradability even at minimal concentrations [1]. Chromium (Cr) is among the most prevalent heavy metal contaminants. Its oxidation state, Cr(VI), harms the environment yet has catastrophic consequences for human health [2]. It is removed by physical and chemical procedures such as ion exchange, chemical precipitation and electrochemical treatment. Yet, most of these procedures have downsides, such as the formation of hazardous sludge, causing disposal issues and the need for costly tools and monitoring systems [3]. Adsorption is regarded an appealing and favourable technology because to its ease of design, simplicity, and high efficiency. Carbon-based nanomaterials have been investigated as superior adsorbents in aqueous solutions for the separation of organic and inorganic contaminants. The current study recommends the usage of adsorbents based on graphene oxide (GO). GO is an oxygen-rich material that is produced during the oxidation of graphite. It features hydrophobic areas due to aromatic groups in the nanosheets' centres, along with a large number of hydrophilic functional groups such as hydroxy, aldehyde, epoxy, and carboxyl groups [4]. The latter allow GO to swell and perform electrostatic functions. Chitosan (Cs) is a great adsorbent since it is inexpensive, is biocompatible, and causes no secondary pollution. Its molecular chains include -NH2 and -OH groups, which can interact with heavy metal ions and give significant adsorption capacity [5]. Silicon dioxide (SiO2) nanoparticles with graphene oxide have better physical and chemical characteristics than graphene oxide-like surface area. Similar research has revealed that the presence of SiO2 increases the adsorbent's adsorption capacity for Cr(VI) [6]. The effect of the pH value, contact time and initial chromium concentration was examined in order to determine the feasibility of Cs/GO@SiO2. Its structure and the morphology were studied in detail by the application of BET, XRD, FTIR and SEM techniques. According to the results, the modification of Cs with GO@SiO2 enhanced the percentage removal of chromium ions, especially, in acidic conditions by using 0.5 g/L of the adsorbent. Experimental data of equilibrium were used to calculate adsorption isotherms. According to thermodynamics the spontaneous nature of their adsorption was confirmed. Overall, the results indicate that Cs/GO@SiO2 can be effectively employed for removal of chromium from aqueous solutions
Process for Leveraging Enterprise Architecture in Information Systems Strategic Planning: A Case of Developing a Strategy and Master Plan for a National Integrated Health Laboratory Information Management System in Uganda
No full textEffective alignment of institutional processes with digital technologies is imperative for enterprises, including those in healthcare. Unfortunately, in the context of health laboratories, the scope and complexity of existing digital health interventions has not allowed incorporation of all data and information needs for all core functions of the laboratory subsector. Besides, several operational and management issues still hinder the effective management of information on laboratory services at the health facility, sub-national, and national levels in low-income countries such as Uganda. Although approaches that support implementation of digital health interventions exist, there is still limited technical guidance on the strategic planning and implementation of complex systems such as a nation-wide integrated Health Laboratory Information Management System (HLIMS). Therefore, this article demonstrates how the strategic planning process of such systems can be strengthened by adopting the thinking pattern of enterprise architecture, as a holistic approach for aligning business processes with digital technologies. It specifically presents a process for leveraging Enterprise Architecture in Information Systems Strategic Planning (EAISSP). The process was instantiated in Uganda’s health laboratory subsector, by using it to formulate an architecture-oriented information systems strategy; and the strategy then guided the design of a master plan for a national integrated HLIMS and its implementation plan. Although EAISSP was tested in the context of the laboratory subsector, it can be contextualized to support other efforts like developing a national e-health strategy or information systems strategy of an enterprise in another sector
The Synergic Effects of Nano Additives on the Mechanical Properties of Green Lightweight Concrete
Full text linkConcrete materials have been commonly used in building and construction industries. However, the process of cement manufacture has long been connected with high consumption of energy and adverse environmental impacts. In this study, in order to produce innovative green concrete material that consumes lower energy, resources and is more eco-friendly, industrial waste by-product fly ash cenosphere has been utilized as lightweight aggregate to replace cement by 73.3 %. In most conducted researches regarding lightweight concrete (LWC) with cenospheres, attempts have been made to improve its physicomechanical properties by the inclusion of fibre materials, while limited studies have been performed to investigate the effects of nano additives, especially the synergic influence of them. Therefore, carbon nanotubes (CNTs) with the dosage of 0.05 %, 0.15 %, 0.45 % and nano silica (NS) with the content of 0.2 %, 0.6 %, 1.0 % by cement weight were used in this study as reinforcing fillers on the LWC. Experiments including flexural strength test, compressive strength test, water absorption and thermogravimetric analysis were carried out to evaluate the mechanical behaviors and the hydration characteristics of the produced LWC. Based on the experimental outcomes, the incorporation of CNTs and NS can effectively enhance both the flexural and compressive strength and reduce the absorbed water weight. The results from the thermogravimetric analysis reveal that the binary presence of CNTs and NS exerts positive impacts on the cement hydration reaction