This paper addresses the crucial need for a non-invasive method for precisely detecting the subsurface defects in wooden logs. The method utilises ground penetrating radar (GPR) technology which has the potential to be implemented in real time in saw mills in the near future. Internal defects such as knots, decays and embedded metals are of great concern for lumber production. Nondestructive scanning of logs using GPR to detect defects in logs prior to sawing can greatly increase the productivity and yield of high value defect-free lumber by optimal orientation of the saw blade, and prevent damage to saw blade from embedded metals. Also, GPR offers high speed scanning capability which is needed for future online implementation in saw mills. In this research, the scanned GPR data have been analysed to investigate the capability of GPR to detect subsurface defects such as metals, decays and knots. This paper presents the comprehensive results from GPR scanning of several wooden logs and shows comparisons with actual condition inside the log observed after sawing the log. The advantages of the GPR technique, experimental setup and data processing used for detection of subsurface defects in logs are explained in detail. This study has shown that GPR can be used to accurately identify the subsurface defects such as knots, decays and metallic nails inside the logs which are not visible from outside observations. The results show that GPR can be a very promising technique for future online implementation in saw mills.

The Paris Agreement’s pressing global mandate to limit global warming to 1.5 degrees Celsius above pre-industrial levels by 2030 has placed immense pressure on energy-consuming industries and businesses to deploy robust, advanced, and accurate monitoring and tracking of carbon footprints. This critical issue is examined through a systematic review of English-language studies (2015–2024) retrieved from three leading databases: Scopus (n = 1528), Web of Science (n = 1152), and GreenFILE (n = 271). The selected literature collectively highlights key carbon footprint tracking methods. The resulting dataset is subjected to bibliometric and scientometric analysis after refinement through deduplication and screening, based on the PRISMA framework. Methodologically, the analysis integrated the following: (1) evaluating long-term trends via the Mann–Kendall and Hurst exponent tests; (2) exploring keywords and country-based contributions using VOSviewer (v1.6.20); (3) applying Bradford’s law of scattering and Leimkuhler’s model; and (4) investigating authorship patterns and networks through Biblioshiny (v4.3.0). Further, based on eligibility criteria, 35 papers were comprehensively reviewed to investigate the emerging carbon footprint tracking technologies such as life cycle assessment (LCA), machine learning (ML), artificial intelligence (AI), blockchain, and data analytics. This study identified three main challenges: (a) lack of industry-wide standards and approaches; (b) real-time tracking of dynamic emissions using LCA; and (c) need for robust frameworks for interoperability of these technologies. Overall, our systematic review identifies the current state and trends of technologies and tools used in carbon emissions tracking in cross-sectors such as industries, buildings, construction, and transportation and provides valuable insights for industry practitioners, researchers, and policymakers to develop uniform, integrated, scalable, and compliant carbon tracking systems and support the global shift to a low-carbon and sustainable economy.

The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid^®V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology.

Industrial process heating furnace operations consume considerable energy in the U.S. manufacturing sector, making it crucial to identify energy efficient strategies due to the growing need to minimize energy usage and emissions. It is important to identify the potential impact of these factors to enable process engineers to operate process heating systems at the maximum possible efficiency. This study examines and identifies the key impact factors that influence the efficiency of process heating systems using MEASUR (v1.4.0), the DOE software tools such as the insulation effectiveness, the burner stoichiometry, cooling medium, thermal storage, and atmospheric gases. Data from a two-fuel-fired heat treatment furnace and an electric arc furnace (EAF) for steelmaking were employed to establish the baseline heat balance models in MEASUR. The fractional factorial design experiment was developed with two-level parameter values and energy efficiency strategies for the heat input into industrial furnaces. The three most significant parameters for the heat input for a fuel-fired industrial furnace, Industrial Furnace A, are excess air percentage or the oxygen percentage in flue gas (OF), average surface temperature (ST), and combustion air temperature (CT). Similarly, for an electric industrial furnace, Industrial Furnace B, the parameters are charge temperature (CHT), average surface temperature (ST), and time open (TO). A comparative analysis was carried out for the fuel-fired and equivalent electric resistance furnaces to identify the prospect of electrification of industrial furnaces relying upon fossil fuels. The study aims to assist industries and designers in making informed decisions regarding industrial furnace upgrades, process optimization, and maintenance investments, resulting in substantial energy and cost savings, and a reduced environmental impact.

Building Energy Management Systems (BEMS) are computer-based systems that aid in managing, controlling, and monitoring the building technical services and energy consumption by equipment used in the building. The effectiveness of BEMS is dependent upon numerous factors, among which the operational characteristics of the building and the BEMS control parameters also play an essential role. This research develops a user-driven simulation tool where users can input the building parameters and BEMS controls to determine the effectiveness of their BEMS. The simulation tool gives the user the flexibility to understand the potential energy savings by employing specific BEMS control and help in making intelligent decisions. The simulation is developed using Visual Basic Application (VBA) in Microsoft Excel, based on discrete-event Monte Carlo Simulation (MCS). The simulation works by initially calculating the energy required for space cooling and heating based on current building parameters input by the user in the model. Further, during the second simulation, the user selects all the BEMS controls and improved building envelope to determine the energy required for space cooling and heating during that case. The model compares the energy consumption from the first simulation and the second simulation. Then the simulation model will provide the rating of the effectiveness of BEMS on a continuous scale of 1 to 5 (1 being poor effectiveness and 5 being excellent effectiveness of BEMS). This work is intended to facilitate building owner/energy managers to analyze the building energy performance concerning the efficacy of their energy management system.

Decarbonizing fossil-fuel usage is crucial in mitigating the impacts of climate change. The burning of fossil fuels in boilers during industrial process heating is one of the major sources of CO₂ in the industry. Electrification is a promising solution for decarbonizing these boilers, as it enables renewable energy sources to generate electricity, which can then be used to power the electric boilers. This research develops a user-driven simulation model with realistic data and potential temperature data for a location to estimate boilers’ current energy and fuel usage and determine the equivalent electrical boiler capacity and energy usage. A simulation model is developed using the Visual Basic Application (VBA)^® and takes factors such as current boiler capacity, steam temperature and pressure, condensate, makeup water, blowdown, surface area, and flue gas information as input. Random numbers generate the hourly temperature variation for a year for discrete-event Monte Carlo Simulation. The simulation generates the hourly firing factor, energy usage, fuel usage, and CO₂ emissions of boilers for a whole year, and the result compares fossil-fuel and electrical boilers. The simulated data are validated using real system data, and sensitivity analysis of the model is performed by varying the input data.