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By-products of essential oils (EOs) in the industry represent an exploitable material for natural and safe antioxidant production. One representative group of such by-products is distilled solid residues, whose composition is properly modulated by the distillation method applied for the recovery of EOs. Recently, in terms of Green Chemistry principles, conventional extraction and distillation processes are considered outdated and tend to be replaced by more environmentally friendly ones. In the present study, microwave-assisted hydro-distillation (MAHD) was employed as a novel and green method for the recovery of EOs from three aromatic plants (rosemary, Greek sage and spearmint). The method was compared to conventional ones, hydro-distillation (HD) and steam-distillation (SD), in terms of phytochemical composition of distilled solid residues, which was estimated by spectrophotometric and chromatographic methods. Total phenolic content (TPC), total flavonoid content (TFC) and antioxidant activity (ABTS, DPPH and FRAP) results highlighted the distilled solid residues as good sources of antioxidants. Moreover, higher antioxidant activity was achieved for MAHD extracts of solid residues in comparison to HD and SD extracts. A metabolomics approach was carried out on the methanolic extracts of solid residues obtained by different distillation methods using LC-MS analysis followed by multivariate data analysis. A total of 29 specialized metabolites were detected, and 26 of them were identified and quantified, presenting a similar phenolic profile among different treatments, whereas differences were observed among different species. Rosmarinic acid was the most abundant phenolic compound in all extracts, being higher in MAHD extracts. In rosemary and Greek sage extracts, carnosol and carnosic acid were quantified in significant amounts, while trimers and tetramers of caffeic acid (salvianolic acids isomers) were identified and quantified in spearmint extracts, being higher in MAHD extracts. The obtained results pointed out that MAHD extracts of distilled solid by-products could be a good source of bioactives with potential application in the food, pharmaceutical and cosmetic industries, contributing to the circular economy.

In situ product removal (ISPR) is required for microbial production of hydrophobic chemicals.Vacuum distillation coupled to a bioreactor is effective for ISPR of volatile products.Process intensification allows low costs for ISPR and product purification.Even products with a boiling point of 170°C can be vacuum distilled from aqueous broth (if they are sufficiently hydrophobic). Many fermentation products inhibit their own microbial production, which complicates industrial-scale fermentation development for these products. When a product is volatile, this inhibition can be circumvented by removing product during fermentation through evaporation in a loop around the bioreactor. Microbes can survive this loop if its temperature is reduced using vacuum. Then, regrowing of microbes is not required. From a separation efficiency viewpoint, the evaporation loop should not use a single equilibrium stage, but a multistage vacuum distillation column. Such in situ product removal (ISPR) by vacuum distillation has hardly been recognized as an option, however. Costs for this product removal with subsequent purification are modest, even when product titers are low. A prerequisite is the use of advanced energy integration and heat pumping methods. Many fermentation products inhibit their own microbial production, which complicates industrial-scale fermentation development for these products. When a product is volatile, this inhibition can be circumvented by removing product during fermentation through evaporation in a loop around the bioreactor. Microbes can survive this loop if its temperature is reduced using vacuum. Then, regrowing of microbes is not required. From a separation efficiency viewpoint, the evaporation loop should not use a single equilibrium stage, but a multistage vacuum distillation column. Such in situ product removal (ISPR) by vacuum distillation has hardly been recognized as an option, however. Costs for this product removal with subsequent purification are modest, even when product titers are low. A prerequisite is the use of advanced energy integration and heat pumping methods.

Mainstream knowledge distillation methods primarily include self-distillation, offline distillation, online distillation, output-based distillation, and feature-based distillation. While each approach has its respective advantages, they are typically employed independently. Simply combining two distillation methods often leads to redundant information. If the information conveyed by both methods is highly similar, this can result in wasted computational resources and increased complexity. To provide a new perspective on distillation research, we aim to explore a compromise solution that aligns complex features without conflicting with output alignment. In this work, we propose to decouple the classifier’s output into two components: non-target classes learned by the student, and target classes obtained by both the teacher and the student. Finally, we introduce Decoupled Classifier Knowledge Distillation (DCKD), where on one hand, we fix the correct knowledge that the student has already acquired, which is crucial for merging the two methods; on the other hand, we encourage the student to further align its output with that of the teacher. Compared to using a single method, DCKD achieves superior results on both the CIFAR-100 and ImageNet datasets for image classification and object detection tasks, without reducing training efficiency. Moreover, it allows relational-based and feature-based distillation to operate more efficiently and flexibly. This work demonstrates the great potential of integrating distillation methods, and we hope it will inspire future research.

A total of eight extraction techniques ranging from conventional methods (hydrodistillation (HD), steam distillation (SD), turbohydrodistillation (THD)), through innovative techniques (ultrasound assisted extraction (US-SD) and finishing with microwave assisted extraction techniques such as In situ microwave-generated hydrodistillation (ISMH), microwave steam distillation (MSD), microwave hydrod-iffusion and gravity (MHG), and microwave steam diffusion (MSDf)) were used to extract essential oil from lavandin flowers and their results were compared. Extraction time, yield, essential oil composition and sensorial analysis were considered as the principal terms of comparison. The essential oils extracted using the more innovative processes were quantitatively (yield) and qualitatively (aromatic profile) similar to those obtained from the conventional techniques. The method which gave the best results was the microwave hydrodiffusion and gravity (MHG) method which gave reduced extraction time (30 min against 220 min for SD) and gave no differences in essential oil yield and sensorial perception.

The Lavandula genus, which includes lavender (Lavandula angustifolia) and lavandin (L. angustifolia × Lavandula latifolia), is cultivated worldwide for its essential oils, which find applications in perfumes, cosmetics, food processing and, more recently, in aromatherapy products. The chemical composition of lavender and lavandin essential oils, usually produced by steam distillation from the flowering stems, is characterized by the presence of terpenes (e.g. linalool and linalyl acetate) and terpenoids (e.g. 1,8-cineole), which are mainly responsible for their characteristic flavour and their biological and therapeutic properties. Lavender and lavandin distilled straws, the by-products of oil extraction, were traditionally used for soil replenishment or converted to a fuel source. They are mineral- and carbon-rich plant residues and, therefore, a cheap, readily available source of valuable substances of industrial interest, especially aroma and antioxidants (e.g. terpenoids, lactones and phenolic compounds including coumarin, herniarin, α-bisabolol, rosmarinic and chlorogenic acids). Accordingly, recent studies have emphasized the possible uses of lavender and lavandin straws in fermentative or enzymatic processes involving various microorganisms, especially filamentous fungi, for the production of antimicrobials, antioxidants and other bioproducts with pharmaceutical and cosmetic activities, opening up new challenging perspectives in white biotechnology applications.

Knowledge Distillation (KD) is one of the most effective and widely used methods for model compression of large models. It has achieved significant success with the meticulous development of distillation losses. However, most state-of-the-art KD losses are manually crafted and task-specific, raising questions about their contribution to distillation efficacy. This paper unveils Learnable Knowledge Distillation (LKD), a novel approach that autonomously learns adaptive, performance-driven distillation losses. LKD revolutionizes KD by employing a bi-level optimization strategy and an iterative optimization that differentiably learns distillation losses aligned with the students’ validation loss. Building upon our proposed generic loss networks for logits and intermediate features, we derive a dynamic optimization strategy to adjust losses based on the student models’ changing states for enhanced performance and adaptability. Additionally, for a more robust loss, we introduce a uniform sampling of diverse previously-trained student models to train the loss with various convergence rates of predictions. With the more universally adaptable distillation framework of LKD, we conduct experiments on various datasets such as CIFAR and ImageNet, demonstrating our superior performance without the need for task-specific adjustments. For example, our LKD achieves 73.62% accuracy with the MobileNet model on ImageNet, significantly surpassing our KD baseline by 2.94%.

Currently, image recognition based on deep neural networks has become the mainstream direction of research; therefore, significant progress has been made in its application in the field of tea detection. Many deep models exhibit high recognition rates in tea leaves detection. However, deploying these models directly on tea-picking equipment in natural environments is impractical; the extremely high parameters and computational complexity of these models make it challenging to perform real-time tea leaves detection. Meanwhile, lightweight models struggle to achieve competitive detection accuracy; therefore, this paper addresses the issue of computational resource constraints in remote mountain areas and proposes Reconstructed Feature and Dual Distillation (RFDD) to enhance the detection capability of lightweight models for tea leaves. In our method, the Reconstructed Feature selectively masks the feature of the student model based on the spatial attention map of the teacher model; it utilizes a generation block to force the student model to generate the teacher’s full feature. The Dual Distillation comprises Decoupled Distillation and Global Distillation. Decoupled Distillation divides the reconstructed feature into foreground and background features based on the Ground-Truth. This compels the student model to allocate different attention to foreground and background, focusing on their critical pixels and channels. However, Decoupled Distillation leads to the loss of relation knowledge between foreground and background pixels. Therefore, we further perform Global Distillation to extract this lost knowledge. Since RFDD only requires loss calculation on feature map, it can be easily applied to various detectors. We conducted experiments on detectors with different frameworks, using a tea dataset collected at the Huangshan Houkui Tea Plantation. The experimental results indicate that, under the guidance of RFDD, the student detectors have achieved performance improvements to varying degrees. For instance, a one-stage detector like RetinaNet (ResNet-50) experienced a 3.14% increase in Average Precision (AP) after RFDD guidance. Similarly, a two-stage model like Faster RCNN (ResNet-50) obtained a 3.53% improvement in AP. This offers promising prospects for lightweight models to efficiently perform real-time tea leaves detection tasks.

Pressure Swing Distillation (PSD) is the only advanced technology that does not require the addition of third components to the system to enhance the separation of azeotropic mixtures. It outperforms homogeneous distillation for separating pressure-sensitive azeotropic mixtures. In this study, we aimed to separate methanol and toluene using the Non-Random Two-Liquid (NRTL) and Aspen Plus thermodynamic calculation models to simulate a binary homogeneous azeotropic system. The standard PSD process was employed to separate methanol and toluene. Furthermore, multiple optimization sequences were utilized to sequentially optimize the process for obtaining higher purities of methanol and toluene while reducing the Total Annual Cost (TAC) and heat energy consumption. The effects of the optimization sequence on the TAC were investigated. The best optimization sequences for graphing in Origin or Aspen Plus were found to be RR1, NR, NF1, NF2, NT1, and NT2. Additionally, the Double-Effect Distillation (DED) optimization sequence is similar, with TAC as the primary function in the simulation and methanol and toluene purities up to 99.99%. In the DED simulation, the feed position and tray number were found to be sensitive to TAC by the order NR > NF1 > NF2 and NT1 > NT2. This study simulated PSD using the NRTL thermodynamic calculation model in Aspen Plus and generated visualizations using Origin software.

Extraction is the first and most important step in obtaining the effective ingredients of medicinal plants. Mentha longifolia (L.) L. is of considerable economic importance as a natural raw material for the food and pharmaceutical industries. Since the effect of different extraction methods (traditional and modern methods) on the quantity, quality and antimicrobial activity of the essential oil of this plant has not been done simultaneously; the present study was designed for the first time with the aim of identifying the best extraction method in terms of these features. For this purpose, extracting the essential oil of M . longifolia with the methods of hydrodistillation with Clevenger device (HDC), steam distillation with Kaiser device (SDK), simultaneous distillation with a solvent (SDE), hydrodistillation with microwave device (HDM), pretreatment of ultrasonic waves and Clevenger (U+HDC) and supercritical fluid (SF) were performed. Chemical compounds were identified by gas chromatography coupled with mass spectrometer (GC-MS). Antimicrobial activity of essential oils against various clinical microbial strains was evaluated by agar diffusion method and determination of the minimum inhibitory concentration and minimum bactericidal concentration (MIC and MBC). The results showed that the highest and lowest yields of M . longifolia leaf essential oil belonged to HDC (1.6083%) and HDM (0.3416%). The highest number of compounds belonged to SDK essential oil and was equal to 72 compounds (with a relative percentage of 87.13%) and the lowest number of compounds was related to the SF essential oil sample (7 compounds with a relative percentage of 100%). Piperitenone (25.2–41.38%), piperitenone oxide (22.02–0%), pulegone (10.81–0%) and 1,8-cineole (5–35.0%) are the dominant and main components of M . longifolia essential oil were subjected to different extraction methods. Antimicrobial activity results showed that the lowest MIC value belonged to essential oils extracted by HDM, SDK, SDE and U+HDC methods with a value of 1000 μg/mL was observed against Gram-negative bacteria Shigella dysenteriae , which was 5 times weaker than rifampin and 7 times weaker than gentamicin. Therefore, it can be concluded that in terms of efficiency of the HDC method, in terms of the percentage of compounds of the HDM method, and in terms of microbial activity, the SDK, HDM and U+HDC methods performed better.

Good-quality drinking water is an essential requirement for a healthy and sustainable future. In the current scenario, people living in remote areas of the world are deficient of potable water, especially in developing nations. Desalination technologies available today are energy intensive and aggravate carbon emissions as most energy requirements are fulfilled by using fossil fuels. Solar still is a simple and direct solar desalination device used for water distillation. The major problem associated with a solar still is its low productivity. The main aim of this review paper is to discuss various modifications in a solar still which resulted in productivity enhancement. Different parameters affecting a passive solar still performance and their optimum values for maximum productivity are also thoroughly analysed in this paper. Water depth is an important operating parameter that influences still productivity, and various results showed that maximum productivity is achieved mostly at minimum water depths.