The production process of a smart factory is complex and dynamic. As the core of manufacturing management, the research into the flexible job shop scheduling problem (FJSP) focuses on optimizing scheduling decisions in real time, according to the changes in the production environment. In this paper, deep reinforcement learning (DRL) is proposed to solve the dynamic FJSP (DFJSP) with random job arrival, with the goal of minimizing penalties for earliness and tardiness. A double deep Q-networks (DDQN) architecture is proposed and state features, actions and rewards are designed. A soft ε-greedy behavior policy is designed according to the scale of the problem. The experimental results show that the proposed DRL is better than other reinforcement learning (RL) algorithms, heuristics and metaheuristics in terms of solution quality and generalization. In addition, the soft ε-greedy strategy reasonably balances exploration and exploitation, thereby improving the learning efficiency of the sc... [more]

The increasing share of variable renewable energies in the power grid is an incentive to explore demand response strategies. Chlor-alkali processes are high potential candidates, according to previous publications. Within Germany’s chemical industry, chlorine production accounts for approximately 20% of electricity use and could play a significant role in power grid stabilisation on the consumer end. This study focuses on the feasibility of load flexibilisation in epichlorohydrin plants, with the second biggest estimated demand response potential for chlorine-based products in Germany. A plant model with allyl chloride storage was created based on real data and literature values. Results from this model, spot market and balancing power prices, and future electricity market scenarios were used in a mixed-integer linear optimisation. We find that benefits from demand response can be generated as soon as additional power and storage volume is provided. The composition of provided types of... [more]

This article is concerned with the problem of leader-following consensus tracking control for multi-unmanned aerial vehicle (UAV) systems under deception attacks and actuator saturation. While guaranteeing the expected control performance, a novel adaptive event-triggered scheme (AETS) is developed to reduce the frequency of data transmission among UAVs and reduce the release of redundant data, where the adaptive threshold can be adjusted online according to the dynamic error instead of giving a default value. With the help of a new Lyapunov function, sufficient conditions and security criteria are developed to ensure that all following UAVs could reach secure consensus asymptotically with a leader UAV. Lastly, an illustrative example is presented to validate the effectiveness of the designed method.

The construction of underground gas storage mostly focuses on depleted gas reservoirs. However, the depleted gas reservoir used to build underground gas storage in China is located far from the main gas consumption economic zone. It is necessary to reconstruct underground gas storage using nearby reservoirs in order to meet the needs of economic development. The complex three-phase seepage characteristics encountered in the process of reconstruction of underground gas storage reservoirs seriously affect their storage and injection production capacities. Combined with the mechanism of multiphase seepage and the multicycle injection production mode during the process of gas storage construction, the feasibility of rebuilding gas storage in medium- and low-permeability reservoirs was evaluated through relative permeability experiments and core injection production experiments. The results showed that the mutual driving of two-phase oil−water systems will affect the storage space and seepa... [more]

Micro-Electric Discharge Machining (μ-EDM) is one of the widely applied micromanufacturing processes. However, it has several limitations, such as a low cutting rate, difficult debris removal, and poor surface integrity, etc. Hybridization of the μ-EDM is proposed as an alternative to overcome the process limitations. Conversely, it complicates the process nature and poses a challenge for modelling and predicting critical process responses. Therefore, in this work, two distinct, nonparametric, previously unreported, workpiece material independent models using a Generalized Regression Neural Network (GRNN) and Gaussian Process Regression (GPR) were developed and compared to assess their performance with limited training data. Various smoothing factors and kernels were tested for GRNN and GPR, respectively. The prediction of models was compared in terms of the mean absolute percentage error, root mean square error, and coefficient of determination. The results showed that GPR outperforms... [more]

Rice storage conditions include location, granary depth, storage time, temperature and atmosphere. The fungi community varies during storage, but how these communities change remains unexplored so far. This study collected rice samples from granaries in different horizontal and vertical directions and storage time over two years. High-throughput ITS (Internal Transcribed Spacer) sequencing analysis revealed that Ascomycota (73.81%), Basidiomycota (6.56%) and Mucoromycota (9.42%) were the main Eumycota present during rice storage. The main fungi communities were Aspergillus sp., Fusarium sp., Rhizopus sp., Gibberella sp., Tilletia sp. and Penicillium sp. The contribution of storage time, horizontal orientation and vertical depth effect on fungi community relative abundance were 17.18%, 5.98% and 0.11%, respectively. Aspergillus sp. was the predominant Eubacterium during this process. The horizontal A was mainly occupied by Paraconiothyrium sp. and the location S, had Clavispora sp. Both... [more]

The performance of low-pressure axial flow fans is directly affected by the three-dimensional bending and twisting of the blades. A new blade design method is adopted in this work, where the radial distribution of blade angle and blade bending angle is composed of standard-form rational quadratic Bézier curves. Dendrite Net is then trained to predict the pneumatic performance of the fan. A non dominated sorting genetic algorithm is employed to solve the global optimization problem of the total pressure coefficient and efficiency. The simulation results show that the optimal blade load distribution along the radial direction becomes uniform, and the suction surface separation vortex and passage vortex are restrained. On the other hand, the tip leakage vortex is enhanced and moves toward the blade leading edge. According to the experimental results, the maximum efficiency increases by 5.44%, and the maximum total pressure coefficient increases by 2.47% after optimization.

TC21 titanium alloy is a new high damage-tolerant structural material for aerospace applications; its welding performance is very critical for the manufacturing of structural parts. In this research, the inertia friction welding (IFW) process of TC21 alloy was conducted, and the microstructure and mechanical properties of IFW joints were investigated. In tensile tests, specially designed tensile samples with grooves were utilized to achieve the tensile properties of IFW joints. The results show that the microhardness and tensile strength of IFW joints are higher than that of the base metal, but the plasticity of joints is worse compared with the base metal. The SEM results reveal that the weld zone has a dynamic recrystallization microstructure with uniform fine-needle α phases. The microstructure evolution in the weld is the key factor that results in the change of the mechanical properties of TC21 IFW joints.

The stress-relief coal mine methane surface gas venthole is considered an effective method by which to realize coal mine methane exploitation and outburst prevention. Existing stress permeability models for caved zones, fractured zones, and bending subsidence zones were embedded into FLAC3D simulation software by using the FISH language. In cooperation with the in-situ data of a mine in a Huainan coalfield, the permeability distribution of pressure-relief surface gas drainage via different zones was simulated. The results indicated that the surface gas ventholes were effective for gas extraction from mining areas. By analyzing the distribution of permeability, three zones were identified: (1) the fully compacted zone, (2) the gradually compacted zone, and (3) the “O” type fractured zone. The seepage path of pressure-relief surface gas drainage was visualized. Most of the gas seeps into the adjacent rock mass at first and then is extracted through surface gas ventholes. Meanwhile, seepa... [more]

Cu-based bimetallic materials have been widely reported as efficient catalysts for electrocatalytic nitrate reduction. However, the faradaic efficiency and selectivity are still far from satisfactory. Herein, Cu-Fe bimetallic nanoalloys with adjustable Cu/Fe ratios are successfully prepared through a reactive mechanical milling approach with CuCl2, FeCl3 and Na as the starting materials. The optimized Cu3Fe exhibits excellent nitrate conversion efficiency of 81.1% and 70.3% ammonia selectivity at −0.7 V vs. RHE within 6 h under 0.1 M Na2SO4 and 100 ppm NO3−. The Fe-introduction-induced upshift of the d-band center is identified to be beneficial for promoting nitrate adsorption on Cu3Fe. Moreover, favorable H generation under the assistance of Fe could effectively accelerate the stepwise hydrogenation during electrocatalytic nitrate reduction, resulting in significantly improved NH4+ selectivity. This work supplies valuable insights for the rational design of transition-metal-based bime... [more]

The etiology of cardiovascular disease is still an unsolved world problem, and high morbidity, disability, and mortality are the main characteristics of cardiovascular diseases. There is, therefore, a need for effective and rapid early prediction of likely outcomes in patients with cardiovascular disease using artificial intelligence (AI) techniques. The Internet of Things (IoT) is becoming a catalyst for enhancing the capabilities of AI applications. Data are collected through IoT sensors and analyzed and predicted using machine learning (ML). Existing traditional ML models do not handle data inequities well and have relatively low model prediction accuracy. To address this problem, considering the data observation mechanism and training methods of different algorithms, this paper proposes an ensemble framework based on stacking model fusion, from Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Extra Tree (ET), Gradient Boosting D... [more]

Stainless steel has a variety of applications nowadays because of its mechanical strength and corrosion resistance. The large-scale machinery made up of stainless steel has an outstanding performance and endurance for manufacturing industries. However, stainless steel scraps accumulate with a lubricant to form sludge during the operation. To reduce the environmental hazards caused by sludge, this research attempts to construct a hydrometallurgical process to recover iron, nickel, and chromium from the sludge. The experiments could be divided into four parts. First, calcination was adopted to remove the oil and water content. The factors that have impacts on the leaching efficiency, such as the type of acid and the calcination temperature, were investigated in the second part. It was optimal that the sludge was calcined at 300 ℃ for 8 h and leached by 4 mol/L HCl. The results revealed that the leaching percentages of iron, nickel, and chromium were 97.6%, 98.1%, and 95.7%, respectively.... [more]

Packaging is an integral part of the food industry associated with food quality and safety including food shelf life, and communications from the marketing perspective. Traditional food packaging provides the protection of food from damage and storage of food products until being consumed. Packaging also presents branding and nutritional information and promotes marketing. Over the past decades, plastic films were employed as a barrier to keep food stuffs safe from heat, moisture, microorganisms, dust, and dirt particles. Recent advancements have incorporated additional functionalities in barrier films to enhance the shelf life of food, such as active packaging and intelligent packaging. In addition, consumer perception has influences on packaging materials and designs. The current trend of consumers pursuing environmental-friendly packaging is increased. With the progress of applied technologies in the food sector, sustainable packaging has been emerging in response to consumer prefer... [more]

In this paper, we propose combining topology optimization with a parametric algorithm (TOCPA) to design an industrial synchronous reluctance motor (SynRM) that satisfies super-premium efficiency (IE4). TOCPA consists of two main processes. An improved niching algorithm is proposed as an existing parametric algorithm, and multiple optimal points were identified quickly and effectively within the design range specified by the user. Existing parametric algorithms lack the ability to find unpredictable new optimal shapes; therefore, topology optimization has been added. TOCPA converges the two optimization methods to compensate for the shortcomings of each and maximizes the advantages. In addition, the boundary surface ON−OFF method is applied to accelerate the topology optimization process. Subsequent ratio-based smoothing techniques smooth out discontinuous interfaces and remove clusters to improve the torque characteristics and reduce the manufacturing difficulty. Finally, the torque ri... [more]

New trends and future prospects for small capacity systems of Renewable Energy-driven DESalination (REDES) are reviewed and assessed in this paper over a nominal desalination capacity range of 3−1000 m3/d. A thorough literature review is reported in order to evaluate current research and developing activities. Outstanding commercial prospects in the near future are identified for two off-grid REDES technologies under development. First, wave energy converters with direct coupling to seawater desalination. Second, solar micro gas turbines with biofuel backup coupled to reverse osmosis (RO) desalination and/or zero liquid discharge water treatment. These systems, as well as mature REDES plants (namely PV/RO and wind turbines/RO), will benefit from forthcoming advances in energy efficiency in the RO process itself. The Closed Circuit RO desalination (CCROTM) concept may be a key configuration for enhancing RE-driven RO desalination. Additionally, opportunities for innovation in seawater R... [more]

Industry 4.0 integrates a series of emerging technologies, such as the Internet of Things (IoT), cyber-physical systems (CPS), cloud computing, and big data, and aims to improve operational efficiency and accelerate productivity inside the industrial environment. This article provides a series of information about the required structure to adopt Industry 4.0 approaches and a brief review of related concepts to finally identify challenges and research opportunities to envision the adoption of so-called digital twins. We want to pay attention to upgrading older systems aiming to provide the well-known advantages of Industry 4.0 to such legacy systems as reducing production costs, increasing efficiency, acquiring better robustness of equipment, and reaching advanced process connectivity.

The growing interest in innovations regarding the treatment of oily wastewater stems from the fact that the oil industry is the largest polluter of the environment. The harm caused by this industry is seen in all countries. Companies that produce such wastewater are responsible for its treatment prior to disposal or recycling into their production processes. As oil emulsions are difficult to manage and require different types of treatment or even combined methods, a range of environmental technologies have been proposed for oil-contaminated effluents, such as gravity separation, flotation, flocculation, biological treatment, advanced oxidation processes, and membranes. Natural materials, such as biopolymers, constitute a novel, sustainable solution with considerable potential for oily effluent separation. The present review offers an overview of the treatment of oily wastewater, describing current trends and the latest applications. This review also points to further research needs and... [more]

This paper presents a newly created mathematical model of thermophilic anaerobic digestion of wheat straw carried out in a 2 dm3 bioreactor for methane production. Two batch processes, with 30 mL/dm3 and 35 mL/dm3 organic load, are carried out—one set for parameter identification and one set for model verification. The identification of model parameter values is based on dynamical experiments. It is fulfilled using two different techniques: deterministic sequential quadratic programming algorithm and metaheuristic genetic algorithm. Verification of the developed mathematical models is conducted based on the different data sets of the process. Both models predict the set of the experimental data for all considered process variables well. Genetic algorithm visually fits the data with a higher degree of accuracy, as confirmed by the numerical results for the objective function value.

Micro-cutting is different from conventional cutting in its mechanics. The workpiece material is not considered to be homogeneous in the micro-cutting process. As a result, it is critical to comprehend how microstructure affects surface integrity, cutting forces, and chip formation. In this paper, we experimented with micro-turning on oxygen-free high-conductivity (OFHC) copper with different microstructures after annealing. Feed rate parameters were smaller than, larger than, and equal to the grain size, respectively. Experimental results show that when the feed rates are equivalent to the grain size, the surface roughness of the machined surface is low and the width of the flake structure on the free surface of chips is minimal, and the explanations for these occurrences are connected to dislocation slip.

Oil−water emulsions are widely generated in industries, which may facilitate some processes (e.g., transportation of heavy oil, storage of milk, synthesis of chemicals or materials, etc.) or lead to serious upgrading or environmental issues (e.g., pipeline plugging, corrosions to equipment, water pollution, soil pollution, etc.). Herein, the sources, classification, formation, stabilization, and separation of oil−water emulsions are systematically summarized. The roles of different interfacially active materials−especially the fine particles−in stabilizing the emulsions have been discussed. The advanced development of micro force measurement technologies for oil−water emulsion investigation has also been presented. To provide insights for future industrial application, the separation of oil−water emulsions by different methods are summarized, as well as the introduction of some industrial equipment and advanced combined processes. The gaps between some demulsification processes and ind... [more]

Machine learning (ML) approaches have risen in popularity for use in many oil and gas (O&G) applications. Time series-based predictive forecasting of hydrocarbon production using deep learning ML strategies that can generalize temporal or sequence-based information within data is fast gaining traction. The recent emphasis on hydrocarbon production provides opportunities to explore the use of deep learning ML to other facets of O&G development where dynamic, temporal dependencies exist and that also hold implications to production forecasting. This study proposes a combination of supervised and unsupervised ML approaches as part of a framework for the joint prediction of produced water and natural gas volumes associated with oil production from unconventional reservoirs in a time series fashion. The study focuses on the pay zones within the Spraberry and Wolfcamp Formations of the Midland Basin in the U.S. The joint prediction model is based on a deep neural network architecture leverag... [more]

Chemical laboratories badly need efficient support for human works when experiments are carried out. Process control and data acquisition at the laboratory scale are still practical challenges among others, due to equipment prices and the relative complexity of the different scientific disciplines. There is, however, a large demand recently for the so-called Internet of Things (IoT), intelligent/smart labeled solutions that also include laboratory equipment items. Such solutions have enormous potential in making research activity and routine laboratory work efficient and easy by implementing proper data acquisition and control for laboratory-scale equipment items. To solve these practical challenges, an efficient and simple solution was designed and completed for the control and data acquisition of a laboratory-scale rectification process by a well-known microcontroller connected to MATLAB/Simulink. The straightforward application of this solution is demonstrated in case study measurem... [more]

The experimental and numerical analysis of pyridine adsorption onto activated carbon cloth in a stirred batch adsorber under transition and turbulent regime is presented in this work. Three-dimensional numerical modeling of the adsorption process was implemented for the identification of local velocity, local concentration, and concentration gradients inside the adsorber. This represents a costly computational effort in comparison with conventional batch adsorption models, as for instance the Langmuir kinetic model. Both types of modeling yield comparable results, but the advantage of the 3D modeling is a more detailed resolution of variables, thus avoiding the perfectly mixed assumption. Varying the agitation rate (30−200 rpm) and pyridine initial concentration (99 to 487 mg/L), several kinetic and transport parameters were reported. Hydrodynamic and mass boundary layers are identified around the activated carbon adsorbent following the trajectory of agitation. Furthermore, the major... [more]

Background: Hinokitiol (β-thujaplicin), isolated from the wood of Chamaecyparis taiwanensis, has a wide variety of biological properties including anti-inflammatory, anti-microbial, and anti-tumor effects. Therefore, hinokitiol has become a frequent additive in oral and other healthcare products. Objectives: Our goal was to determine the anti-tumor activity of hinokitiol on human papillomavirus (HPV) positive (n = 3) and negative (n = 2) cell lines derived from cervical or head and neck squamous cell carcinoma (HNSCC) and keratinocyte cell lines (n = 3) transformed spontaneously or with HPV16E6 and E7 oncogenes. Methods: The cell-lines were exposed to hinokitiol at different concentrations (0−200 µM) for 24 h. Cell metabolism, proliferation, and the cell cycle distribution were assessed by MTT- and 3H-thymidine incorporation and flow cytometry. Expressions of p21 and on HPV16E6 and E7 oncogenes were assessed by qPCR. Results: In all carcinoma cell lines, hinokitiol treatment declined t... [more]

Levulinic acid (LA), a platform chemical with high added value, can be obtained by deep hydrolysis of cellulose, but accompanied by the production of formic acid (FA). Due to its high water content, the recovery of levulinic acid and formic acid from aqueous solution consumes a lot of energy in industry. This paper will use the method of reactive extraction to explore the optimal conditions for the recovery of levulinic acid and formic acid from deep hydrolysate. First, the kinetic and thermodynamic parameters of the reaction process were studied. Then, the effects of different parameters, such as temperature, catalyst dosage, and raw material ratio, on the reaction extraction process were investigated. Finally, through the simulation and optimization of the process, the optimized recovery conditions were chosen to realize the recovery of formic acid and levulinic acid. It is found that reactive extraction can achieve the purpose of efficiently separating levulinic acid and formic acid... [more]