Search Results (136)

Agro-industrial activities require adaptations of technological energy systems to align with the European Sustainable Development Goals, and their highly seasonal and intermittent consumption profiles necessitate precise environmental assessment. This study aims at investigating the photovoltaic (PV) energy in various existing olive mills to assess the reduction in olive oil carbon footprint (CF) when it is supplied by either a rooftop PV system or by PV combined with a battery energy storage system (BESS) to promote the self-consumption of the renewable energy produced, compared to the case when electricity is supplied by the national grid (NG). To this end, an algorithm was developed to optimise a decision-making tool for low-carbon energy systems in agro-industrial activities. An economic assessment was performed to complement the decision-making process. The potential energy self-consumed by the mill ranged between 11% and 18.1%. The renewable energy produced covered between 11% and 84.7% of the mill’s energy consumption. CF reduction resulted between 22% and 119%, depending on the system boundaries considered. The proposed methodology allows for replicability to other industrial activities, having different energy consumption profiles, with seasonal and discontinued consumption paths, since it is based on an hourly energy consumption evaluation. Full article

The growing demand for automated production systems is driving continuous innovation in smart and data-driven manufacturing technologies. In the field of production metrology, the trend is shifting from using measurement laboratories to integrating measurement systems directly into production processes. This has led the Institute of Manufacturing Technology at TU Vienna together with its partners to develop a roughness measurement device that can be directly integrated into machine tools. Building on this foundation, this study tries to find applications beyond mere surface roughness assessment and demonstrates how the device could be applied in broader contexts of manufacturing process monitoring. By linking surface measurements with tool wear monitoring, the study establishes a correlation between surface roughness and wear progression of indexable inserts in milling. It demonstrates how in situ data can support predictive maintenance and the real-time adjustment of cutting parameters. This represents a first step toward integrating in situ metrology into closed-loop control in machining. The experimental setup followed ISO 8688-1 guidelines for tool life testing. Indexable inserts were operated throughout their entire service life while surface roughness was continuously recorded. In parallel, cutting edge conditions were documented at defined intervals using focus variation microscopy. The results show a consistent three-phase pattern: initially stable roughness, followed by a steady increase due to flank wear, and an abrupt decrease in roughness linked to edge chipping. These findings confirm the potential of integrated roughness measurement for condition-based monitoring and the development of adaptive machining strategies. Full article

Due to the globalization of supply chains and the resulting increase in the quantity and diversity of suppliers, the segmentation of suppliers has become fundamental to improving relationship management and supplier performance. Moreover, given the need to incorporate sustainability into supply chain management, criteria based on economic, environmental, and social performance have been adopted for evaluating suppliers. However, few studies present sustainable supplier segmentation models in the literature, and none of them make it possible to predict individual supplier performance for each TBL dimension in a non-compensatory manner. Moreover, none of them permits the use of historical performance data to adapt the model to the usage environment. Given this, this study aims to propose a decision-making model for sustainable supplier segmentation using an adaptive network-based fuzzy inference system (ANFIS). Our approach combines three ANFIS computational models in a tridimensional quadratic matrix, based on diverse criteria associated with the triple bottom line (TBL) dimensions. A pilot application of this model in a sugarcane mill was performed. We implemented 108 candidate topologies using the Neuro-Fuzzy Designer of the MATLAB^® software (R2014a). The cross-validation method was applied to select the best topologies. The accuracy of the selected topologies was confirmed using statistical tests. The proposed model can be adopted for supplier segmentation processes in companies that wish to monitor and/or improve the sustainability performance of their suppliers. This study may also be helpful to researchers in developing computational solutions for segmenting suppliers, mainly regarding ANFIS modeling and providing appropriate topological parameters to obtain accurate results. Full article

This study tackles the challenge of achieving high-precision robotic machining of elastic materials, where elastic recovery and overcutting often impair accuracy. To address this, a novel milling strategy, RAPSO, is introduced by combining an adaptive particle swarm optimization (APSO) algorithm with a reinforcement learning (RL)-based compensation mechanism. The method builds a material-specific milling model through residual error characterization, incorporates a dynamic inertia weight adjustment strategy into APSO for optimized toolpath generation, and integrates a Proximal Policy Optimization (PPO)-based RL module to refine trajectories iteratively. Experiments show that RAPSO reduces residual material by $33.51\%$ compared with standard PSO and APSO methods, while offering faster convergence and greater stability. The proposed framework provides a practical solution for precision machining of elastic materials, offering improved accuracy, reduced post-processing requirements, and higher efficiency, while also contributing to the theoretical modeling of elastic recovery and advanced toolpath planning. Full article

Selecting appropriate milling equipment is an important means to reduce carbon emissions and improve the efficiency of part-machining processes, as the process of machining the same part on different milling machines varies greatly. Traditional milling machine selection approaches only involve a static analysis of their advantages and disadvantages without considering the dynamic changes in the production process, making them difficult to adapt to the requirements of the new era. To solve this problem, we establish a milling machine selection model based on the new quality productivity (NQP) concept; propose a calculation method considering carbon emissions, efficiency, and quality (expressed as surface roughness) in the production process; and quantitatively analyze the process objectives of different milling machines according to the changes in the machining process. The spindle speed, feed rate, cutting width, and cutting depth are taken as the optimization variables, and the cutting parameters are optimized using the egret swarm algorithm (ESA) to obtain the Pareto frontier solutions providing low-carbon and high efficiency process parameters. The method was verified through a plane milling example. After ESA optimization, the processing time was increased by 5.6%, the surface roughness accuracy was improved by 12.9%, and the carbon emissions were reduced by 13.1%, demonstrating the effectiveness of the proposed method. Full article

Robotic timber joinery demands integrated, adaptive methods to compensate for the inherent dimensional variability of wood. We introduce a seamless robotic workflow to enhance the measurement accuracy of the Workpiece Coordinate System (WCS). The approach leverages a Zivid 3D camera mounted in an eye-in-hand configuration on a KUKA industrial robot. The proposed algorithm applies a geometric method that strategically crops the point cloud and fits planes to the workpiece surfaces to define a reference frame, calculate the corresponding transformation between coordinate systems, and measure the cross-section of the workpiece. This enables reliable toolpath generation by dynamically updating WCS and effectively accommodating real-world geometric deviations in timber components. The workflow includes camera-to-robot calibration, point cloud acquisition, robust detection of workpiece features, and precise alignment of the WCS. Experimental validation confirms that the proposed method is efficient and improves milling accuracy. By dynamically identifying the workpiece geometry, the system successfully addresses challenges posed by irregular timber shapes, resulting in higher accuracy for timber joints. This method contributes to advanced manufacturing strategies in robotic timber construction and supports the processing of diverse workpiece geometries, with potential applications in civil engineering for building construction through the precise fabrication of structural timber components. Full article

The increasing global demand for resilient, sustainable agricultural systems has intensified the need for advanced monitoring strategies, particularly for climate-adaptive crops such as Moringa oleifera Lam. This study presents an integrated approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral and thermal cameras to monitor the vegetative performance and determine the optimal harvest period of four M. oleifera genotypes in a Mediterranean environment. High-resolution data were collected and processed to generate the NDVI, canopy temperature, and height maps, enabling the assessment of plant vigor, stress conditions, and spatial canopy structure. NDVI analysis revealed robust vegetative growth (0.7–0.9), with optimal harvest timing identified on 30 October 2024, when the mean NDVI exceeded 0.85. Thermal imaging effectively discriminated plant crowns from surrounding weeds by capturing cooler canopy zones due to active transpiration. A clear inverse correlation between NDVI and Land Surface Temperature (LST) was observed, reinforcing its relevance for stress diagnostics and environmental monitoring. The results underscore the value of UAV-based multi-sensor systems for precision agriculture, offering scalable tools for phenotyping, harvest optimization, and sustainable management of medicinal and aromatic crops in semiarid regions. Moreover, in this study, to produce M. oleifera leaf powder intended for use as a food ingredient, the leaves of four M. oleifera genotypes were dried, milled, and evaluated for their hygiene and safety characteristics. Plate count analyses confirmed the absence of pathogenic bacterial colonies in the M. oleifera leaf powders, highlighting their potential application as natural and functional additives in food production. Full article

Rock hardness significantly impacts comminution efficiency, one of mining’s most energy-intensive processes. Accurate, rapid, and non-invasive hardness characterization can enhance mine-to-mill optimization and energy management. This study investigates sensor-based technologies, hyperspectral imaging, and portable X-ray fluorescence (pXRF) integrated with machine learning (ML) algorithms for characterizing rock hardness in open-pit gold mining contexts. A total of 159 rock samples from two Canadian open-pit gold mines were analyzed through Leeb rebound hardness (LRH), short-wave infrared (SWIR) hyperspectral imaging, and a pXRF analyzer for chemical characterization. The most critical spectral features of SWIR images were extracted using a novel and automated feature extraction approach and further refined by applying a recursive feature elimination (RFE) algorithm to reduce the dimensionality of the spectral feature space. Three ML algorithms, including Random Forest Regressor (RFR), Adaptive Boosting (AdaBoost), and Multivariate Linear Regression (MLR), were applied to develop predictive hardness models considering three scenarios: using chemical features, using refined spectral features, and their combination. The findings underscore the potential of advanced sensor integration and analytics in remotely characterizing rock hardness, which could contribute to enhancing efficiency and sustainability in modern mining operations. Full article

The integration of acoustic emission (AE) signals into adaptive control systems for CNC wood milling represents a promising advancement in intelligent manufacturing. This study investigated the feasibility of using AE signals for the real-time monitoring and control of CNC milling processes, focusing on medium-density fiberboard (MDF) as the workpiece material. AE signals were captured using dual-channel sensors during side milling on a five-axis CNC machine, and their characteristics were analyzed across varying spindle speeds and feed rates. The results showed that AE signals were sensitive to changes in machining parameters, with higher spindle speeds and feed rates producing increased signal amplitudes and distinct frequency peaks, indicating enhanced cutting efficiency. The statistical analysis confirmed a significant relationship between AE signal magnitude and cutting conditions. However, limitations related to material variability, sensor configuration, and the narrow range of process parameters restrict the broader applicability of the findings. Despite these constraints, the results support the use of AE signals for adaptive control in wood milling, offering potential benefits such as improved machining efficiency, extended tool life, and predictive maintenance capabilities. Future research should address signal variability, tool wear, and sensor integration to enhance the reliability of AE-based control systems in industrial applications. Full article

Olive oil production generates vast quantities of by-products, with olive mill wastewater (OMW) being a particularly challenging effluent. Characterized by its dark color, high acidity, and rich composition of organic matter, phenolic compounds, and residual oils, OMW resists conventional degradation methods and poses significant environmental risks due to its phytotoxicity and microbial inhibition. Addressing this issue requires sustainable solutions that align with circular economy principles. A promising strategy involves the biotechnological valorization of OMW using the non-conventional yeast Yarrowia lipolytica, which thrives on organic-rich substrates and converts them into high-value metabolites. This review provides a comprehensive analysis of recent advances in Y. lipolytica applications for OMW valorization, emphasizing its role in developing eco-friendly industrial processes. It begins by outlining the physicochemical challenges of OMW and the metabolic versatility of Y. lipolytica, including its ability to adapt to acidic, phenolic-rich environments. Subsequent sections critically evaluate the yeast’s capacity to synthesize commercially valuable products such as lipases (used in the food and biofuel industries), citric acid (a food and pharmaceutical additive), and polyols like mannitol and erythritol (low-calorie sweeteners). Strategies to optimize microbial productivity, such as substrate pre-treatment, nutrient supplementation, and process engineering, are also discussed. By synthesizing current research, the review highlights how Y. lipolytica-driven OMW valorization can mitigate environmental harm while creating economic opportunities, bridging the gap between waste management and green chemistry. Full article

The estimation of available active power in coal-fired thermal power plant units involves considerable complexity and remains a critical task for plant operators. To avoid compromising system stability, operators often operate the thermal unit below its full capacity. To address this issue, the aim of this paper is to facilitate the process of estimating the maximum active electrical power by applying an assistive system based on ANFIS (Adaptive Neuro-Fuzzy Inference System), a method that combines the strengths of neural networks and fuzzy logic. Since the generated electric energy is directly linked to the amount of thermal energy produced, the analysis is focused on the boiler combustion process. It has been shown that the key factors in this process are the coal mills and their achievable capacity, as well as the calorific value of coal. Therefore, the proposed assistive system is based on the estimation of the available capacity of each active mill, which is then combined with the estimated calorific value of the coal to determine the achievable active electrical power of the unit. The conducted analysis and experiments confirm the validity of this approach. Full article

Weed management is a persistent challenge in Southeast Asian rice production, particularly in direct-seeded rice (DSR), due to the diversity of weed species and variable field and environmental conditions that can compromise weed control, necessitating innovative solutions. An integrated weed seed impact mill (iWSIM) reduces weed seed banks by destroying weed seeds during the harvest process. This mixed study is the first to fully explore the applicability of iWSIM technology in Southeast Asian rice systems, focusing on both combine harvester and iWSIM specifications and operation, determinants of efficacy, and field and harvest conditions. Weed seed bank reduction with an iWSIM depends on several factors, including weed seed retention and subsequent capture by the combine at harvest, weed seed separation into the chaff fraction, and the iWSIM’s efficacy against weed seeds captured in the chaff fraction. Observations from Southeast Asia indicate variable seed retention among key weed species, presenting challenges for harvesting strategies and iWSIM effectiveness. To optimize the iWSIM efficacy, recommendations include larger fields to reduce the weed seed produced on bunds, achieving complete early-season weed control, lowering the harvest header height to about 15 cm to capture more weed seeds, cleaning mechanism adjustments to ensure weed seeds are retained in the chaff fraction, and greater combine harvester engine power to allow a lower header height and power the iWSIM. The estimated weed control benefits of the iWSIM should also be weighed against additional equipment operating costs. iWSIM technology holds promise as part of a sustainable solution for weed control in Southeast Asian rice, contingent upon further region-specific research and adaptation. Full article

The great capability of additive manufacturing to produce parts with complex, even impossible to achieve, geometries through five-or-more-axis machining or other conventional processes opened a promising future decades ago. For its part, mature subtractive manufacturing presents problems of material waste, especially relevant in the case of superalloys used in fields such as aerospace. From the need to overcome the limitations of both and take advantage of their capabilities, the new paradigm of hybrid additive and subtractive manufacturing was born, which today is defined as a hybrid flow of subprocesses that interact with the part in the same machine. This paper presents a review of the emerging trends in additive–subtractive manufacturing over the last five years. This review has been carried out by applying an adaptation of the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) methodology to the field of manufacturing engineering. Specifically, open access papers published in English between 2020 and 2024, collected in prestigious journals (classified as Q1 and Q2 within the ranking of their respective categories according to the Journal Citation Report), and peer-reviewed conference proceedings of recognized prestige have been selected. From the analysis of the selected articles, it is concluded that hybrid additive and subtractive manufacturing is especially focused on the aerospace field, using titanium and nickel alloys, combining processes among which DED (directed energy deposition) and milling stand out. Full article

Ensuring efficient long-term quality control of the raw mix remains a priority for the cement industry, supporting initiatives to lower the CO₂ footprint by incorporating significant amounts of alternative fuels and raw materials in clinker production. This study presents an effective method for creating a robust auto-tuner for proportional–integral–differential (PID) controller control of the lime saturation factor (LSF) of the raw mix using artificial neural networks (ANNs). This auto-tuner, combined with a previously studied robust PID controller, forms an integrated system that adapts to process changes and maintains low long-term variance in LSF. The ANN links each of the three PID gains to the process dynamic parameters, with the three ANNs also interconnected. We employed the Levenberg–Marquardt method to optimize the ANNs’ synaptic weights and applied the weight decay method to prevent overfitting. The industrial implementation of our control system, using the auto-tuner for 16,800 h of raw mill operation, shows an average LSF standard deviation of 2.5, with fewer than 10% of the datasets exceeding a standard deviation of 3.5. Considering that the measurement reproducibility is 1.44 and assuming a low mixing ratio of the raw meal in the silo equal to 2, the LSF standard deviation in the kiln feed approaches the analysis reproducibility, indicating that disturbances in the raw meal largely diminish in the kiln feed. In conclusion, integrating traditional, well-established tools like PID controllers with newer advanced techniques, such as ANNs, can yield innovative solutions. Full article

Effective grinding of residual agricultural materials helps to improve yield in the production of chemical compounds through hydrothermal technology. Milling pretreatment has different types of pre-treatment where ball mills, roller mills, and finally, the knife mill stand out. The knife mill being a mill with continuous processing, its multiple benefits and contributions highlight the knife milling process; however, it is a process that is generally carried out with dry biomass that generates extra processing of the biomass before grinding, implying longer times and wear than other equipment. This work presents the design of a knife mill with an adaptation of free convection drying as a joint process of knife milling and drying. The design is based on lignocellulosic biomass, and the knife milling results are presented for two biomasses: peapods and coffee cherries. The knife mill is designed with a motor, a housing with an integrated drive system, followed by a knife system and a feeding system with a housing and finally the free convection drying system achieving particle sizes in these biomasses smaller than 30 mm, depending on the time processed. The data demonstrate the significant impact of particle size on the yields of various platform chemicals obtained from coffee cherry and peapod waste biomass. For coffee cherry biomass, smaller particle sizes, especially 0.5 mm, result in higher total yields compared to larger sizes while for peapod biomass at the smallest particle size of 0.5 mm, the total yield is the highest, at 45.13%, with notable contributions from sugar (15.63%) and formic acid (19.14%). Full article