Search Results (169)

Building Information Modeling (BIM)-enabled performance assessment workflows for rural constructions remain relatively unexplored. This is even more important for buildings implementing process-oriented systems, such as airflow networks. This study presents a BIM-integrated framework for the early-stage design and performance assessment of rural constructions, with application to solar barn dryers and their ventilation systems through reduced-order airflow-network modeling. The proposed workflow combines parametric BIM-based geometry generation with lumped-parameter fluid-dynamic modeling to evaluate the influence of airflow-network topology on pressure losses, airflow distribution, fan power demand, and energy consumption. Nine BIM-generated design alternatives and ten geometric parameter sets were investigated under equivalent operating conditions. The airflow system was represented as a pressure-driven network including solar air panels, ducts, collectors, fan chambers, ventilation channels, and drying cells, accounting for both localized and distributed pressure losses. Results show that airflow-network geometry significantly affects system performance. Configurations characterized by more compact and aerodynamically efficient layouts reduced cumulative pressure losses by approximately 10–20% compared with less optimized solutions. More efficient designs enable reductions in required airflow rates of ~22% and in fan power demand of up to ~40% (≈11–18 kW). The most efficient configurations also exhibited lower annual energy consumption while maintaining the minimum overpressure required for effective hay drying. The study demonstrates how BIM environments can support physics-informed comparative evaluation of alternative ventilation layouts during the early design stage, extending BIM applications toward performance-oriented design and digital management of agricultural building systems. The proposed methodology provides a computationally efficient design-support framework that may also apply to other controlled-environment agricultural infrastructures governed by airflow-network dynamics. Full article

The convective drying of agricultural materials is an energy-intensive process, and optimizing dryer design is critical for improving efficiency and product quality. This study presents a comprehensive heat and mass transfer model for the convective drying of alfalfa leaves in a rotary drum dryer. Freshly harvested leaves with an initial moisture content of approximately 70% (w.b.) were used as the test material. The proposed system features a simplified drum design aimed at enhancing process efficiency while reducing equipment complexity. The primary objective was to reduce the moisture content of alfalfa leaves to below 50% to ensure their quality during subsequent storage and transportation. To determine the optimal operating conditions, the kinematics of leaf motion inside the rotating drum and the associated heat and mass transfer phenomena were investigated through analytical modeling, numerical simulation, and experimental studies on a laboratory-scale physical model. An analytical model was developed to establish relationships between transverse kinematic characteristics (detachment condition, Froude number, drum inclination angle), average longitudinal velocity, and residence time. Numerical simulations based on the Navier–Stokes equations (continuity, momentum, and energy) provided detailed moisture content distributions within individual leaves under varying airflow orientations and drying durations. The novelty of this work lies in the integrated determination of optimized operating parameters through combined analytical, numerical, and experimental approaches. A regression model relating final moisture content to key process variables (air velocity, temperature of 60 °C, drum rotation frequency and mass of loaded material) was developed from experimental data, yielding practical recommendations for the design and operation of rotary drum dryers for alfalfa and similar agricultural materials. Full article

Water hyacinth (Eichhornia crassipes) is an invasive aquatic plant with high lignocellulosic content, offering potential as a natural fiber resource for craft-based industries. However, its extremely high initial moisture content (≈95%) presents a major challenge in fiber processing, particularly for small-scale industries that rely on traditional sun-drying methods. These methods are highly dependent on weather conditions, prone to contamination, and produce inconsistent fiber quality. This study adopts a research and development (R&D) approach to design and evaluate an innovative dryer machine specifically for water hyacinth fiber processing. The proposed system utilizes LPG-based heating and controlled airflow to achieve stable drying conditions. Experimental results show that the dryer machine can process 10 kg of wet water hyacinth within 280 min, significantly shorter than the approximately four days required for manual drying. The system reduces the moisture content to below 10%, resulting in improved fiber cleanliness, uniformity, and usability. Although the dried mass produced by the machine is slightly lower compared to manual drying, this is attributed to more effective moisture removal, leading to lower residual water content in the final product. Productivity analysis indicates improved operational consistency and higher processing capacity over extended periods (30–180 days), particularly under varying weather conditions. These findings demonstrate that controlled drying technology provides a reliable and efficient solution for lignocellulosic fiber processing in small-scale industries, contributing to improved material utilization and sustainable biomass management. Full article

Understanding mass transfer mechanisms during coffee drying is essential for optimizing postharvest processing conditions. This study aimed to estimate the effective moisture diffusivity coefficient ($D_{eff}$) and convective mass transfer coefficient ($h_m$) of Coffea arabica L. var. Catimor through mathematical modeling. Drying experiments were conducted in an automated convection dryer (CE 130) at four constant temperatures (40, 50, 60, and 70 °C) and an air velocity of 1.5 m/s. Drying time, equilibrium moisture content ($M_{eq}$), and critical moisture content ($M_c$) were determined. A MATLAB based algorithm was developed to solve Fick’s second law and estimate $D_{eff}$ and $h_m$ during coffee drying. An Arrhenius-type model was further applied to describe the temperature dependence of both coefficients. Although the model captured the general increasing trend with temperature, only moderate coefficients of determination were obtained (R² = 0.63 for $D_{eff}$ and R² = 0.74 for $h_m$). Drying times were 26.5, 17.5, 16.5, and 13.5 h at 40, 50, 60, and 70 °C, respectively. $M_{eq}$ ranged from 11.30% to 11.76% (wet basis), while $M_c$ remained close to 46% (wet basis) at all temperatures. $D_{eff}$ ranged from $0.232\times {10}^{-10} \mathrm{to} 1.397\times {10}^{{-}^{10}} {\mathrm{m}}^2/\mathrm{s}$, whereas $h_m$ ranged from 1$.668\times {10}^{-9} \mathrm{to} 3.998\times {10}^{-9} \mathrm{m}/\mathrm{s}$. Both coefficients increased with temperature, indicating enhanced internal diffusion and external mass transfer. These findings provide useful engineering parameters for modeling, design, and optimization of industrial coffee drying processes. Full article

Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap, and the measured data may also include distortions such as spikes, drops, and noise. To address these issues, this study presents a multi-task triple-hybrid deep learning framework that handles appliance classification and anomaly detection together. The model brings together 1D-CNN, BiLSTM, and Transformer Attention so that local patterns, temporal dependencies, and wider contextual information can be learned within the same structure. It also uses a dual-output design to classify appliance categories and detect anomaly types simultaneously. Experiments were carried out on Building 1 of the UK-DALE dataset with four appliances: kettle, microwave, washer dryer, and fridge freezer. For the anomaly task, synthetic disturbances were added to segmented signal windows and grouped as normal, spike, drop, and noise. To check how well the proposed framework handled different scenarios, it was tested on both the UK-DALE and REDD datasets. Looking at the main UK-DALE results, the model correctly identified appliances 99.48% of the time and spotted anomalies with 98.80% accuracy. A secondary test on the REDD dataset yielded an 86.44% classification score. This proves the architecture can adjust to completely new power grid environments without losing its edge. On top of that, when pitted against standard benchmark models like Seq2Point, this triple-hybrid design clearly does a better job of mapping out complex signal changes. As a result, it yields much stronger anomaly detection metrics. Full article

Non-professional repairs could contribute to an increase in repairs, supporting circular economy goals, yet they raise safety concerns. While recent regulatory actions promote repair, they focus mainly on advancing access to professional repair, rather than enabling repairs by non-professionals, such as consumers and volunteers. This study investigates how product design can mitigate repair safety risks and contribute to safe non-professional repairs. In this exploratory research, a washing machine and a washer-dryer combo were analyzed through repeated disassembly and reassembly to identify mechanical, electrical, thermal, and chemical risks occurring during and after repair, as well as risk-inducing and risk-preventing design features and product architecture. Identified risk-inducing features include exposed components, vulnerable electrical and water connections, lack of reassembly guidance, and differences between disassembly and reassembly sequences. Based on the analysis insights, we developed a first version of a method for identifying, assessing, and mitigating repair safety risks. The method provides elements to visualize repair safety risks as an additional layer on disassembly maps and preliminary design recommendations to mitigate risk. The repair-risk mapping method aims to support designers in analyzing and anticipating repair safety risks and identifying relevant risk-inducing design features. Our findings show that relatively small design interventions, such as visual guidance, reusable connectors, robust connections, and enforced disassembly and reassembly sequences, can reduce risks and make repairs more accessible to non-professionals. Our research suggests that product design plays a crucial role in expanding repair opportunities to a broader range of users while maintaining safety. Full article

To address the industry pain points of domestic traditional fish meal processing equipment, such as low protein retention, low drying efficiency, and poor operational reliability, this study focuses on high-moisture, heat-sensitive cod meal as the test material to investigate the structural improvement and synergistic optimization of process parameters for vacuum low-temperature fish meal dryers. The conventional uniform-pitch heating coil was optimized into a three-section differentiated structure, with a wear-resistant protective structure additionally incorporated to fundamentally resolve issues including insufficient heat transfer at the feed end, coking at the discharge end, and coil wear-induced leakage. Verification via COMSOL Multiphysics simulation revealed that the axial temperature gradient of the optimized equipment decreased from 8.6 °C/m to 6.2 °C/m, while the thermal fatigue life of the coil was extended from 2–3 years to over 10 years. A three-factor, three-level response surface methodology (RSM) was employed to design the experiments, with the heating temperature, vacuum degree, and drying time as independent variables and the fish meal protein content as the response variable. A total of 17 experimental runs were constructed, including 12 factorial points and 5 central points; each run was replicated three times in parallel, and data were reported as mean values. Analysis of variance (ANOVA) demonstrated that the regression model was highly statistically significant (p < 0.0001), with a coefficient of variation (CV) of 0.2464% and a coefficient of determination (R²) of 0.9944, indicating excellent fitting accuracy. The determined optimal process parameters were as follows: a drying temperature of 65 °C, vacuum degree of 0.08 MPa, and drying time of 75 min. Compared with the traditional process, the optimized process shortened the drying cycle by 37.5%, reduced unit energy consumption by 29.2%, and increased the fish meal protein content by 6.6%. This research provides a reliable technical solution for the localized processing of high-end fish meal. Full article

Industrial control of volatile organic compound (VOC) emissions from medium-density fibreboard (MDF) production remains constrained by a shortage of compound-resolved evidence from full-scale plants, where wood furnish, amino resin chemistry, heat transfer, gas flow, and wet gas cleaning act simultaneously. Here, we analysed more than 20,000 synchronized operating records from a full-scale single-stage flash-tube MDF dryer at an industrial SWISS KRONO production line and linked total VOC (TVOC) measurements from flame ionization detection with Fourier-transform infrared speciation on the cleaned stack. Five compounds—α-pinene, 3-carene, limonene, methanol, and formaldehyde—accounted for more than 80% of the resolved VOC signal. Process–state contrasts showed that higher digester residence time, discharge screw speed, adhesive amount, urea amount, dryer inlet temperature, and scrubber–water temperature increased one or more representative compounds, whereas higher hardwood share, additional flue-gas supply, and higher scrubber–water pH decreased them. Limonene, methanol, and formaldehyde were substantially more process-sensitive than α-pinene. An exploratory decorrelation step further showed that a drying/throughput domain explained about half of the variability of the screened process space. The study therefore identifies the small set of compounds and operating domains that most strongly govern the cleaned dryer-stack signature and provides a mechanistically grounded prioritization framework for follow-up causal experiments, source apportionment, and emission-mitigation design in industrial MDF manufacture. Unlike product or chamber emission studies, this work links the compound-resolved FTIR/FID chemistry of the final cleaned industrial stack with synchronized production variables; it therefore addresses a scale-integration gap by transforming routine compliance-type exhaust monitoring into a process-diagnostic framework for ranking emission sources, abatement-sensitive variables, and mitigation experiments. Full article

The drying kinetics of three varieties of cassava were evaluated in a tray dryer, using a completely randomized design with a three-factor factorial arrangement: temperature (50, 60, and 70 °C), air velocity (1, 2, and 3 m/s), and variety (“Blanca Mona”, “Ica Negrita”, “Venezolana”), with three replicates per treatment. The results obtained were used to construct drying curves, which showed that this process occurred in the decreasing period. The drying curves were adjusted to mathematical models, and the Page model was the best fit to the experimental data with R²_adj values closer to 1 and RSS values less than 0.0086. The effective diffusivities (D_eff) in cassava flours were represented by the Arrhenius equation with values ranging from 5.24 × 10⁻¹⁰ to 1.58 × 10⁻⁹ m²/s. The activation energy (E_a) recorded values between 20.34 and 28.32 kJ/mol. The flours from the three cassava varieties were obtained under the best drying conditions (70 °C and 3 m/s). The physicochemical characterization of fresh roots and flours from three cassava varieties revealed significant genotype-dependent differences in their proximal composition. Blanca Mona exhibited the highest ash content and the lowest total carbohydrates among fresh roots, while Ica Negrita stood out for its superior crude fiber content in flour. Venezolana flour stood out for its higher protein content (3.86 ± 0.04 g/100 g) and significant fiber content (1.39 ± 0.39 g/100 g), making it the flour with the best nutritional profile and greatest potential for food applications. Therefore, tray drying is recommended as one of the suitable methods for cassava flour production. Full article

It is well known that gas sensor responses are affected by the presence of humidity in the analyzed gas. This is particularly true when dealing with biological fluid samples, whose high moisture content interferes with the adsorption of the trace volatile organic compounds (VOCs) on the sensors’ active layer. To address this challenge, this study focuses on designing and testing a novel sampling system for the dehumidification of biological fluid headspace to be characterized by an electronic nose (e-Nose). Such a system, based on the use of disposable polymeric sampling bags purged with dry air, exploits the polymers’ permeability to water vapor to reduce sample humidity. Tested materials included Nalophan^TM (20 μm), high-density polyethylene (HDPE, 8, 9, 10 and 11 μm), low-density polyethylene (LDPE, 12 and 50 μm), and biodegradable polyester (Bio-PS, 15 μm). First, dehumidification performance was characterized as a function of dry air flow rate and film type. A purge of 1 L/min accelerated the sample humidity removal compared to passive storage of bags from >2 h to <1 h (from 80% to 20% RH). Second, a mass-balance model was applied to dedicated experiments to decouple water losses due to diffusion and adsorption, showing that diffusion through the polymer wall dominates, while adsorption occurs in the early stages of conditioning. Third, because these materials are not selectively permeable to water, potential loss of water-soluble VOCs during dehumidification was investigated. Pooled urine headspace samples—both raw and spiked with a metabolite mix of VOCs—were dried using each material and analyzed using a photo-ionization detector (PID) and an e-Nose. Results were compared against a Nafion^TM dryer. Comparison was based on the e-Nose’s ability to discriminate between pooled vs. spiked samples and reveal real-life metabolomic changes. Nalophan^TM bags and Nafion^TM dryer provided the highest VOC fingerprint to support discrimination by the e-Nose, while Bio-PS provided the fastest sample dehumidification. The proposed bag-based system offers a cost-effective, disposable, and contamination-free solution to humidity interference in e-Noses. Full article

The high-performance control of nonlinear industrial plants in a wide operation range requires intelligent techniques. The aim of the present research is to develop an engineering approach for adaptation of the gains of the well-mastered and widely applied linear PID controller based on an offline-trained backpropagation artificial neural network (BANN) that assesses the plant parameters for the current operation point. The controller’s gains are online-computed from the empirical relationship with the plant parameters. Robust stability and robust performance conditions are derived for the gain-scheduled BANN-PID system. Their fulfilment ensures system feasibility in an industrial environment. The approach is demonstrated for the control of temperature in a laboratory dryer for fruits. The BANN training is based on data derived and validated from experiments using the Takagi–Sugeno–Kang nonlinear plant model. Simulations show that the BANN-PID system outperforms both the gain-scheduled fuzzy logic PID control system, designed in previous research, and the PID real-time control system by reducing overshoot six times and settling time 1.8 times and improving robustness 1.3 times. Full article

The chili pepper (Capsicum annuum) is among the most widely consumed vegetables worldwide, valued for its sensory and nutritional properties. Nevertheless, it is highly vulnerable to deterioration due to its elevated moisture content. Effective preservation strategies, such as the addition of salt combined with drying, are therefore crucial to maintaining quality and extending shelf life. This study employed a modified Reaction Engineering Approach (REA) to model the drying kinetics and temperature behavior of chili paste under continuous and intermittent conductive hydro-drying conditions. Thirty experiments were conducted considering various salt concentrations (0, 7.5 and 15 g salt/100 g paste), water temperatures in the hydro-dryer, and heating intermittency through on/off cycles. The modified REA model accurately predicted both moisture and temperature profiles, with determination coefficients of 0.9463 and 0.8820, respectively. In addition to direct validation with the complete dataset, cross-validation between cayenne and jalapeño varieties demonstrated the ability of the model to generalize across different formulations and structural characteristics. These results confirm the robustness of the proposed framework and its suitability as a predictive tool for heterogeneous food matrices. Direct and cross-validation confirmed strong predictive performance across all operating conditions and both chili varieties, supporting the use of the modified REA model as a robust tool for representing coupled moisture–temperature dynamics in conductive hydro-drying of semi-solid matrices. Overall, the model provides a reliable platform for analyzing, designing, optimizing, and controlling hydro-drying processes in semi-solid foods, supporting the development of more efficient and sustainable preservation strategies. Full article

Hot-air drying is widely adopted for herbs because it is robust and easy to control, yet it is often energy-intensive and may operate far from optimal conditions when industrial dryers rely on fixed airflow paths and large air recirculation rates. This work investigates a conventional basket-type, adiabatic hot-air dryer through an instrumented 30 h drying campaign and a psychrometric energy analysis. The hot-air drier is designed to reduce the relative humidity of herbs from the environmental value (highly variable as a function of the species, the weather conditions, and, mostly, the seasonality) to 20%. Temperature and relative humidity were measured at four positions to characterize the shelf-by-shelf drying sequence and to identify process phases. A mass balance indicated that approximately 3.8 t of water was removed during the trial. Based on the measured thermodynamic states of the moist air and estimated airflow rates (35,000–53,000 m³/h), the baseline configuration was analyzed and an upgrade strategy was proposed to improve dehumidification and overall efficiency while preserving the conventional hot-air-drying concept. The alternative solution integrates a refrigeration-based dehumidification loop (heat pump) to decouple moisture removal from sensible heating; three plant layouts and seasonal boundary conditions (summer/winter) were simulated. For the most favorable configurations, the specific final–primary energy demand and the associated CO₂-equivalent emissions were reduced by about 70–85% compared with the baseline, depending on the airflow rate and recirculation strategy. The results highlight practical retrofit options for existing herb dryers and provide a transparent framework for translating measured psychrometric states into energy and emission indicators. The results, achieved and discussed in this study, were used to optimize the utilization of an already existing and operative hot-air dryer. Based on the proposed working configuration, the dryer now allows achieving the fixed target for herb mixtures of the previous configuration and, at the same time, reducing the energy consumption and associated equivalent CO₂ emitted, as well as achieving process completion in less time. Full article

This paper presents the design and industrial implementation of an adaptive discrete control system for a rotary dryer operating in potash fertilizer production. The drying process is characterized by high inertia, multivariable interactions, transport delay, and non-stationary behavior resulting from variations in raw material properties and external disturbances, which significantly reduce the effectiveness of conventional fixed-parameter controllers. A discrete-time mathematical model of the rotary drying process was developed using industrial experimental data collected from a full-scale production plant. The process was modeled as a coupled 2 × 2 multivariable system with pronounced time-delay effects in the main control channels. System identification was carried out using statistical and frequency-domain methods to capture the dominant dynamic characteristics required for controller synthesis. Based on the identified model, an adaptive discrete controller with online parameter adjustment was developed to regulate outlet moisture content and exhaust gas temperature. Simulation and industrial results confirmed stable operation under varying conditions, improved regulation accuracy, enhanced process stability, and an average production efficiency increase of approximately 1.8%, accompanied by reduced fuel consumption. Full article

In order to improve the control accuracy and adaptability of drying control systems in complex paddy rice drying processes, the Gaussian process regression model predictive (GPR-MPC) drying process control strategy is designed. The strategy integrates the advantages of drying mathematical models and artificial intelligence algorithms. Firstly, based on the predicted moisture content of the drying mathematical model and the moisture content detection value, the Gaussian process regression is used to establish the model of moisture content prediction error. Secondly, the GPR-MPC control system is designed and simulation experiments are conducted to verify its effectiveness. Finally, the GPR-MPC intelligent control system of a grain dryer is designed and drying experiments are conducted with the grain dryer. The GPR-MPC intelligent control system testing experiment is conducted using a 15-ton cross-flow batch type recirculating grain dryer. The experimental result shows that the maximum, average, and variance of the grain moisture content control error are 0.4%, 0.165% and 0.114%, respectively. Compared to the MPC control system, the designed GPR-MPC intelligent control system has high prediction accuracy, small moisture content control error, and stable control system operation. The integration of drying mathematical models and artificial intelligence algorithms can effectively improve the drying effect and reduce dependence on data volume. This research is of great significance for promoting the development of intelligent drying technology. Full article