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Data-driven fault diagnosis, identifying abnormality causes using collected industrial data, is one of the challenging tasks for intelligent industry safety management. It is worth noting that practical industrial data are usually related to a mixture of several physical attributes, such as the operating environment, product quality and working conditions. However, the traditional models may not be sufficient to leverage the coherent information for diagnostic performance enhancement, due to their shallow architecture. This paper presents a hierarchical matrix factorization (HMF) that relies on a succession of matrix factoring to find an efficient representation of industrial data for fault diagnosis. Specifically, HMF consecutively decomposes data into several hierarchies. The intermediate hierarchies play the role of analysis operators which automatically learn implicit characteristics of industrial data; the final hierarchy outputs high-level and discriminative features. Furthermore, HMF is also extended in a nonlinear manner by introducing activation functions, referred as NHMF, to deal with nonlinearities in practical industrial processes. The applications of HMF and NHMF to fault diagnosis are evaluated by the multiple-phase flow process. The experimental results show that our models achieve competitive performance against the considered shallow and deep models, consuming less computing time than deep models.

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It is uncertain how the application of artificial intelligence (AI) technology transforms industrial work. We address this question from the perspective of cognitive systems, which, in this case, includes considerations of AI and process transparency, resilience, division of labor, and worker skills. We draw from a case study on glass tempering that includes a machine-vision-based quality control system and an advanced automation process control system. Based on task analysis and background literature, we develop the concept of hybrid intelligence that implies balanced AI transparency that supports upskilling and resilience. So-called fragmented intelligence, in turn, may result from the combination of the complexity of advanced automation along with the complexity of the process physics that places critical emphasis on expert knowledge. This combination can result in the so-called "double black box effect", given that designing for understandability for the line workers might not be feasible: expert networks are needed for resilience.

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Propionic acid is, after acetic, formic and 2-ethylhexanoic acids, the fourth largest industrially produced carboxylic acid in terms of capacity. In the last decades the technology to produce propionic acid has evolved and more efficient, i. e. more sustainable processes, based on the hydroformylation of ethylene to propionaldehyde and subsequent oxidation with O2 to propionic acid are capturing the growing capacity for this product. For this reason, a perspective is given on developments in sustainable production of propionic acid and derivatives on industrial scale, which is based on a recently published chapter on the same topic in the Ullmann' Encyclopedia of Industrial Chemistry.

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This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery's LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.

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Modern industrial processes often exhibit large-scale and nonlinear characteristics. Incipient fault detection for industrial processes is a big challenge because of the faint fault signature. To improve the performance of incipient fault detection for large-scale nonlinear industrial processes, a decentralized adaptively weighted stacked autoencoder (DAWSAE) -based fault detection method is proposed. First, the industrial process is divided into several sub-blocks and local adaptively weighted stacked autoencoder (AWSAE) is established for each sub-block to mine local information and obtain local adaptively weighted feature vectors and residual vectors. Second, the global AWSAE is established for the whole process to mine global information and obtain global adaptively weighted feature vectors and residual vectors. Finally, local statistics and global statistics are constructed based on local and global adaptively weighted feature vectors and residual vectors to detect the sub-blocks and the whole process, respectively. The advantages of proposed method are verified by a numerical example and Tennessee Eastman process (TEP).

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Food waste is a serious problem with negative environmental and economic consequences. Unused food (either as waste or by-products and referred to as food residues in the present work) is a source of carbohydrates, lipids, proteins, vitamins, minerals and bioactive compounds that could be used in an alternate or secondary life cycle to avoid discarding it. The present work reviews the potential use of food residues for the bioengineering of single-cell protein (SCP), addressing aspects of production, nutrition and safety, as well as the main challenges and perspectives. SCP is obtained from various microorganisms, including fungi, bacteria, yeasts and algae, in pure or mixed form. SCP generally contains a higher percentage of protein (30-80%) compared to soy (38.6%), fish (17.8%), meat (21.2%) and whole milk (3.28%). SCP is a source of essential amino acids, including methionine, threonine and lysine. The use of food residues as substrates for the production of SCP would reduce production costs (35-75%); however, optimization and industrial scaling are some of the main challenges to its sustainable production. The use food waste and agro by-products from the food industry could be a promising alternative to obtain protein according to a circular production scheme.

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Lactide dimer is an important monomer produced from lactic acid dehydration, followed by the prepolymer depolymerization process, and subsequent purification. As lactic acid is a chiral molecule, lactide can exist in three isomeric forms: L-, D-, and meso-lactide. Due to its time-consuming synthesis and the need for strict temperature and pressure control, catalyst use, low selectivity, high energy cost, and racemization, the value of a high purity lactide has a high cost in the market; moreover, little is found in scientific articles about the monomer synthesis. Lactide use is mainly for the synthesis of high molar mass poly(lactic acid) (PLA), applied as bio-based material for medical applications (e.g., prostheses and membranes), drug delivery, and hydrogels, or combined with other polymers for applications in packaging. This review elucidates the configurations and conditions of syntheses mapped for lactide production, the main properties of each of the isomeric forms, its industrial production, as well as the main applications in the market.

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In the present paper, a manufacturing cell in the presence of faults, coming from the devices of the process, is considered. The modular modeling of the subsystems of the cell is accomplished using of appropriate finite deterministic automata. The desired functionality of the cell as well as appropriate safety specifications are formulated as eleven desired languages. The desired languages are expressed as regular expressions in analytic forms. The languages are realized in the form of appropriate general type supervisor forms. Using these forms, a modular supervisory design scheme is accomplished providing satisfactory performance in the presence of faults as well guaranteeing the safety requirements. The aim of the present supervisor control scheme is to achieve tolerance of basic characteristics of the process coordination to upper-level faults, despite the presence of low-level faults in the devices of the process. The complexity of the supervisor scheme is computed.

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In the process industry, it is essential to establish a data-driven soft sensor to predict the key variable that is difficult to online measure directly. The accuracy performance of data-driven soft sensors relies heavily on data. Unfortunately, it is hard to acquire sufficient and informative data from the samples with limited number, which is called as the small sample problem. For handling the small sample problem, it is a good solution to generating virtual samples according to the distribution of original data. This paper proposes an enhanced method of virtual sample generation utilizing manifold features to develop soft sensors using small data. First, T-Distribution Stochastic Neighbor Embedding (t-SNE) is utilized to extract the features of input data. The main idea of generating virtual samples is to use the interpolation algorithm to obtain virtual t-SNE input features and then the random forest algorithm is utilized to get the virtual outputs using virtual t-SNE input features. Finally, virtual samples using the proposed t-SNE based virtual sample generation (t-SNE-VSG) can be achieved. For the sake of confirming the effectiveness and feasibility of the presented t-SNE-VSG, a standard data set is first used. What is more, a small data set from an actual industrial process of Purified Terephthalic Acid is used to establish a soft sensor model. The results from simulations show that the accuracy performance of the soft sensor established with small data can be effectively improved by adding the virtual samples generated by t-SNE-VSG. In addition, t-SNE-VSG achieves superior accuracy to state-of-the-art virtual sample generation methods.

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Volatile organic compounds (VOCs) source profiles can be used for a number of purposes, such as creating speciated air pollutant emission inventories and providing inputs to receptor and air quality models. In this study, we first collected and schematically evaluated more than 500 Chinese domestic source profiles from literature and field measurements, and then established a most up-to-date dataset of VOCs source profiles in China by integrating 363 selective VOCs profiles into 101 sector-based source profiles. The profile dataset covers eight major source categories and contains 447 VOCs species including non-methane hydrocarbons (NMHCs) species and oxygenated VOCs (OVOCs) species. The results shown that (1) VOCs composition characteristics exhibit variations for most Level-II source sectors and Level-III sub-sectors even under the same Level-I source category; (2) OVOCs, which were significantly missing in previous profiles, account for more than 95% in cooking and 20- 40% in non-road mobile, biomass burning and solvent use sources; (3) aromatics account for 20%-40% in most emission sources except cooking source, alkenes and alkynes account for ~20% in combustion sources (stationary combustion, mobile source and biomass burning), alkanes are abundant in gasoline-related emission sources(on-road mobile source and fuel oil storage and transportation); (4) missing OVOCs species could bring 30%-50% to ozone formation potentials in most emission sources; and (5) there are considerable differences in VOCs chemical groups and individual species for most emission sources between this dataset and the widely used U.S. SPECIATE database, indicating the importance of developing domestic VOCs source profiles. The dataset developed in this study can help support reactive VOCs species-based ozone control strategy and provide domestic profile data for source apportionment and air quality modeling in China and other countries or regions with similar emission source characteristics.

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Eighteen polycyclic aromatic hydrocarbons (PAHs), 24 n-alkanes, 7 hopanes, 2 cholestanes, inorganic ions, elements and carbon fractions were analyzed in real-world source samples of PM2.5 (fine particulate matter) from traffic emissions (gasoline vehicles-TGV, diesel vehicles-TDV, diesel ship-TDS, and heavy oil ships-THOS), coal combustion (coal-fired industrial boilers-CIB, power plants-CPP, and residential stoves-CRS), industrial process emissions (cement industry-IPCI, and steel industry-IPSI), and dust (soil dust-DSD, road dust-DRD, and construction dust-DCD). High molecular weight (sum of five to seven rings) PAHs accounted for higher fractions for TGV (80%) and THS (61%) than for TDV, TDS and coal combustion sources (31%-47%). Hopane ratios (C29αß/C30αß) in coal related sources were mostly higher than 1, whereas that of traffic emissions was lower than 1. The homohopane index [S/(S + R)], which is a useful index for identifying the maturity of fuels, ranked as TGV > THS > TDV and TDS > coal combustion. For n-alkane profiles, coal related sources showed peaks at C16-C19, TDV, TDS and THS showed similar peaks at C17-C25, but peaks for DSD (C30-C32), DRD (C17-C20, C24-25 and C30-C31), CRS (C16-C18 and C28-C29) and TGV (C24-C26) are different. Organic markers were selected which can best differentiate the subtypes within source categories by considering the component levels and variations. Through a comprehensive review, we showed that it is inadvisable to directly use diagnostic ratios for source attribution, although their trends can assist in identifying influential sources.

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Lignin modifying enzymes from fungi and bacteria are potential biocatalysts for sustainable mitigation of different potentially toxic pollutants in wastewater. Notably, the paper and pulp industry generates enormous amounts of wastewater containing high amounts of complex lignin-derived chlorinated phenolics and sulfonated pollutants. The presence of these compounds in wastewater is a critical issue from environmental and toxicological perspectives. Some chloro-phenols are harmful to the environment and human health, as they exert carcinogenic, mutagenic, cytotoxic, and endocrine-disrupting effects. In order to address these most urgent concerns, the use of oxidative lignin modifying enzymes for bioremediation has come into focus. These enzymes catalyze modification of phenolic and non-phenolic lignin-derived substances, and include laccase and a range of peroxidases, specifically lignin peroxidase (LiP), manganese peroxidase (MnP), versatile peroxidase (VP), and dye-decolorizing peroxidase (DyP). In this review, we explore the key pollutant-generating steps in paper and pulp processing, summarize the most recently reported toxicological effects of industrial lignin-derived phenolic compounds, especially chlorinated phenolic pollutants, and outline bioremediation approaches for pollutant mitigation in wastewater from this industry, emphasizing the oxidative catalytic potential of oxidative lignin modifying enzymes in this regard. We highlight other emerging biotechnical approaches, including phytobioremediation, bioaugmentation, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based technology, protein engineering, and degradation pathways prediction, that are currently gathering momentum for the mitigation of wastewater pollutants. Finally, we address current research needs and options for maximizing sustainable biobased and biocatalytic degradation of toxic industrial wastewater pollutants.

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The automatic, sensor-based assessment of human activities is highly relevant for production and logistics, to optimise the economics and ergonomics of these processes. One challenge for accurate activity recognition in these domains is the context-dependence of activities: Similar movements can correspond to different activities, depending on, e.g., the object handled or the location of the subject. In this paper, we propose to explicitly make use of such context information in an activity recognition model. Our first contribution is a publicly available, semantically annotated motion capturing dataset of subjects performing order picking and packaging activities, where context information is recorded explicitly. The second contribution is an activity recognition model that integrates movement data and context information. We empirically show that by using context information, activity recognition performance increases substantially. Additionally, we analyse which of the pieces of context information is most relevant for activity recognition. The insights provided by this paper can help others to design appropriate sensor set-ups in real warehouses for time management.

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Due to the extremely complex mechanism and strong non-linear characteristics of industrial processes, data-driven soft sensor technologies play a key role in the intelligent measurement of process industries. However, the information of the collected process data in the steady stage is quite limited and unreliable, causing the small sample problem. As a result, it becomes an intractable challenge to catch the nature of the process and build accurate soft sensor models. To solve this problem, this paper proposes a novel manifold learning based virtual sample generation method (Isomap-VSG) to generate feasible virtual samples in the information gaps for supplementing the original small sample space. To find data sparse regions reasonably, one kind of manifold learning methods called Isomap is used to visualize process data with high dimension. Then virtual samples can be generated by the interpolation method and extreme learning machine. The simulation results on a standard dataset and a real-world application demonstrate that, compared with other advanced methods, the proposed Isomap-VSG method can achieve better performance in terms of generating feasible virtual samples and improving the accuracy of soft sensor models using limited samples.

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The goal of this study was to determine the impact of industrial processes on the digestion of six milk protein matrices using the harmonized INFOGEST in vitro static digestion protocol. First, this method was optimized to simple protein matrices using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and size exclusion chromatography (SEC) to compare the intestinal protein hydrolysis obtained with increasing quantities of pancreatin. Similar results were achieved with the originally required pancreatin amount (trypsin activity of 100 U.mL-1) and with a quantity of pancreatin equivalent to a trypsin activity of 27.3 U.mL-1, which was thus used to perform the in vitro digestion of the milk matrices. Molecular weight profiles, peptide heterogeneity from LC-MS/MS data, calcium, free amino acid, and peptide concentrations were determined in the gastric and intestinal phases to compare the milk protein digests. Results showed that the industrial process affected not only the protein distribution of the matrices but also most likely the protein structures. Indeed, differences arose in terms of peptide populations generated when the caseins were reticulated or when their calcium concentrations were reduced.

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Microbes from the three domains of life, Bacteria, Archaea, and Eukarya, share the need to sense and respond to changes in the external and internal concentrations of protons. When the proton concentration is high, acidic conditions prevail and cells must respond appropriately to ensure that macromolecules and metabolic processes are sufficiently protected to sustain life. While, we have learned much in recent decades about the mechanisms that microbes use to cope with acid, including the unique challenges presented by organic acids, there is still much to be gained from developing a deeper understanding of the effects and responses to acid in microbes. In this perspective article, we survey the key molecular mechanisms known to be important for microbial survival during acid stress and discuss how this knowledge might be relevant to microbe-based applications and processes that are consequential for humans. We discuss the research approaches that have been taken to investigate the problem and highlight promising new avenues. We discuss the influence of acid on pathogens during the course of infections and highlight the potential of using organic acids in treatments for some types of infection. We explore the influence of acid stress on photosynthetic microbes, and on biotechnological and industrial processes, including those needed to produce organic acids. We highlight the importance of understanding acid stress in controlling spoilage and pathogenic microbes in the food chain. Finally, we invite colleagues with an interest in microbial responses to low pH to participate in the EU-funded COST Action network called EuroMicropH and contribute to a comprehensive database of literature on this topic that we are making publicly available.

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Nowadays, Lithium-ion batteries are widely used in advanced technological devices and Electric and Hybrid Vehicles, due to their high energy density for weight, reduced memory effect and significant number of supported charging/discharging cycles. As a consequence, the production and the use of Lithium-ion batteries will continuously increase in the near future, focusing the global attention on their End-of-Life management. Unfortunately, wasted Lithium-ion batteries treatments are still under development, far from the optimization of recycling processes and technologies, and currently recycling represents the only alternative for the social, economic and environmental sustainability of this market, able to minimize toxicity of End-of-Life products, to create a monetary gain and to lead to the independence from foreign resources or critical materials. This paper analyses the current alternatives for the recycling of Lithium-ion batteries, specifically focusing on available procedures for batteries securing and discharging, mechanical pre-treatments and materials recovery processes (i.e. pyro- and hydrometallurgical), and it highlights the pros and cons of treatments in terms of energy consumption, recovery efficiency and safety issues. Target metals (e.g. Cobalt, Nickel and Lithium) are listed and prioritized, and the economic advantage deriving by the material recovery is outlined. An in-depth literature review was conducted, analysing the existing industrial processes, to show the on-going technological solutions proposed by research projects and industrial developments, comparing best results and open issues and criticalities.

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Light hydrocarbons (C1 -C3 ) are used as basic energy feedstocks and as commodity organic compounds for the production of many industrially necessary chemicals. Due to the nature of the raw materials and production processes, light hydrocarbons are generated as mixtures, but the high-purity single-component products are of vital importance to the petrochemical industry. Consequently, the separation of these C1 -C3 products is a crucial industrial procedure that comprises a significant share of the total global energy consumption per year. As a complement to traditional separation methods (distillation, partial hydrogenation, etc.), adsorptive separations using porous solids have received widespread attention due to their lower energy costs and higher efficiency. Extensive research has been devoted to the use of porous materials such as zeolites and metal-organic frameworks (MOFs) as solid adsorbents for these key separations, owing to the high porosity, tunable pore structures, and unsaturated metal sites present in these materials. Recently, porous organic framework (POF) materials composed of organic building blocks linked by covalent bonds have also shown excellent properties in light hydrocarbon adsorption and separation, sparking interest in the use of these materials as adsorbents in separation processes. This Minireview summarizes the recent advances in the use of POFs for light hydrocarbon separations, including the separation of mixtures of methane/ethane, methane/propane, ethylene/ethane, acetylene/ethylene, and propylene/propane, while highlighting the relationships between the structural features of these materials and their separation performances. Finally, the difficulties, challenges, and opportunities associated with leveraging POFs for light hydrocarbon separations are discussed to conclude the review.

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The residue concentrations and congener profiles of polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) were examined in fly ash and bottom ash released from different thermal industrial processes in Vietnam. PCDD/F concentrations and toxic equivalents (TEQs) in the ash samples varied greatly and decreased in the following order: steel making > aluminum recycling > medical waste incinerator > boilers > municipal waste incinerator > tin production > brick production > coal-fired power plant. Both the precursor and de novo synthesis were estimated as possible formation mechanisms of dioxins in the ash, but the latter pathway was more prevalent. The highest emission factors were estimated for the ash released from some steel-making plants, aluminum-recycling facilities, and a medical waste incinerator. The emission factors of PCDD/Fs in ash released from some steel plants of this study were two to six times higher than the UNEP Toolkit default value. The annual emission amount of ash-bound dioxins produced by 15 facilities in our study was estimated to be 26.2 to 28.4 g TEQ year-1, which mainly contributed by 3 steel plants. Health risk related to the dioxin-containing ash was evaluated for workers at the studied facilities, indicating acceptable risk levels for almost all individuals. More comprehensive studies on the occurrence and impacts of dioxins in waste streams from incineration and industrial processes and receiving environments should be conducted, in order to promote effective waste management and health protection scheme for dioxins and related compounds in this rapidly industrializing country.

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