Harsh Industrial Environments Research Articles

Carbon monoxide-resistant hydrogen detection: Pt–melamine–formaldehyde with portable bluetooth in fuel cell

Hydrogen sensors are critical in fuel cell applications, where carbon monoxide (CO) poisoning presents a substantial limitation to sensor longevity. The broad flammability range of hydrogen (from percentages to ppm) underscores the need for precise and efficient detection methods. Traditional approaches employ catalysts, but suffer from limitations such as poor control over catalyst characteristics, limited robustness, and slow response times, under CO poisoning. This study introduces an innovative approach for hydrogen detection in extreme environments by controlling the hydroxyl (OH-) functional groups and significantly decreasing the Pt particle size from 10 nm to 2 nm, which enhances catalytic activity and stability. This enhancement enables wide-range detection and rapid response even under CO poisoning. Our results demonstrate the development of a superior Bluetooth-enabled hydrogen-cap sensor that operates reliably at room temperature in various harsh industrial environments, highlighting its potential applicability and robustness in real-world scenarios.

LAS-on-edge: A real-time laser absorption spectroscopic water vapor sensor on edge computing platforms

Real-time and in situ water vapor (H2O) sensors are increasingly demanded to indicate thermochemical parameters of energy systems. As an accurate, fast and non-intrusive sensing technology, laser absorption spectroscopy (LAS) enables simultaneous measurement of gas concentration and temperature. Due to the complex spectral computation, real-time and rapid LAS signal processing are still challenging. Most state-of-the-art LAS sensors utilize high-level computing units to post-process laser measurements in real time, hindering their portable deployment in harsh industrial environments. To address the above challenge, we developed an online LAS gas sensor on edging computing platforms, named as LAS-on-Edge. The developed sensor achieves an online monitoring rate up to 62.5 Hz by deploying a spectral-informed neural network on an embedded device. Simultaneously, it reserves the raw kilo-Hz measurements offline for underlying dynamics understanding. The LAS-on-Edge sensor’s compactness also enables its flexible and in situ integration. The sensor is firstly used to monitor a slow mist diffusion and accumulation process, exhibiting a high measurement accuracy with low spectra recovery residuals below 0.01. It is further applied to online monitor the H2O concentration and temperature variation during a lab-scale propane combustion process. It demonstrates a minimal temperature deviation of 1.86 % compared to the thermocouple reference in the flame. The sensor also successfully detects the fundamental flame pulsation at 5.65 Hz as well as high-frequency pulsations exceeding 200 Hz, thereby effectively reveals underlying flame dynamic behaviors.

Online Monitoring of Iron Ore Pellet Size Distribution Using Lightweight Convolutional Neural Network

An improved U-Net based convolutional neural network (CNN) is proposed in this paper for online size monitoring of iron ore pellets. The proposed CNN model is a lightweight version of U-Net. In the proposed model each convolution block of the encoder subnet consists of a concatenated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 1$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$</tex-math> </inline-formula> filter kernel, a batch normalization layer, and a ReLU nonlinearity. The proposed CNN model efficiently detects the pellets even under variable illumination conditions and images with overlapping pellets. The proposed model is evaluated using images of iron ore pellets acquired at the discharge end of a pelletizer disc. The proposed CNN model performs satisfactorily using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 39% less learnable parameters, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 16% reduced computational cost than the original U-Net model. The proposed model outperforms the conventional U-Net, different variants of U-Nets, FCN, and SegNet in weighted IoU, BF Score, global accuracy, SSIM, and PSNR. Experimental validation also shows a good agreement between the obtained pellet size distribution and the manual sieving result. This makes the proposed algorithm suitable for online pellet size-distribution measuring instruments. This method provides a more accurate and fast size monitoring system for iron ore pellets, ideal for real-time industrial applications. The image dataset and source codes are available at the below given link. https://github.com/aryadeo/Pellet_Size_Distribution. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Due to the advent of digitalization in industries, several attempts are being made to use innovative industrial monitoring systems in steel manufacturing plants. Most of the pellet plants in steel industries use human supervision for pellet growth monitoring. In this paper, a camera-based pellet size distribution monitoring method is proposed. A novel deep learning-based algorithm is used for the detection of pellets from the captured image followed by computation of pellet size analysis. Due to low computational complexity and better accuracy, the proposed method is suitable for online pellet size distribution system development compared to other previously proposed methods. However, protection from the harsh industrial environment is required for the uninterrupted and continuous operation of the system. As a future scope, the obtained pellet size information can be used as one of the control variables in designing a control system for the automated operation of the pelletizer disc.

Sensor-Based Target Positioning Technologies in Industrial Automation

This thesis examines the critical role of sensor-based target positioning technologies in industrial automation within the context of Industry 4.0, highlighting their importance for operational efficiency and adaptability. Emphasizing the significance of sensors in automation, the study explores the principles, capabilities, and constraints of RFID, LiDAR, and WSN technologies, each pivotal for accurate target localization. It presents a comparative analysis of these technologies, focusing on their applications across various industrial scenarios, such as production lines, warehousing, and logistics, and evaluates their effectiveness through a series of experiments. The experimental analysis reveals RFID's precision improvement within 10 centimeters and LiDAR's enhanced accuracy with light information integration. It also shows that WSN's precision is contingent on noise levels, with the distance-corrected iterative localization algorithm significantly reducing localization error. The study concludes that while advancements have been made, future research should address the limitations observed in harsh industrial environments, with an emphasis on machine learning and AI integration for data processing to navigate environmental challenges.

Exploring elevated temperature fretting wear behaviour of wrought and laser powder bed fusion IN718 superalloy

The study examines the fretting wear of the Laser Powder Bed Fusion (L-PBF) processed IN718 alloy under gas turbine-like conditions. It involves a large (>106) test cycle investigation at 550 °C and ∼800 N. The heat-treated L-PBF IN718 material exhibited higher hardness compared to heat-treated wrought IN718 due to the formation of finer precipitation of γ' and γ'' in the FCC nickel matrix. A custom wear test setup simulates the harsh industrial environment, revealing a two-fold lower wear coefficient in the L-PBF alloy due to improved hardness and a finer microstructure. The friction coefficient remains marginally different, with the L-PBF alloy showing lower values during the initial phase. The wear mechanisms are similar, but the wrought IN718 HT exhibits severe oxidation-assisted galling.

Study of Anticorrosion and Antifouling Properties of a Cu-Doped TiO2 Coating Fabricated via Micro-Arc Oxidation.

As a promising material for petroleum industrial applications, titanium (Ti) and its alloys receive wide attention due to their outstanding physicochemical properties. However, the harsh industrial environment requires an antifouling surface with a desired corrosion resistance for Ti and its alloys. In order to achieve the desired antifouling properties, micro-arc oxidation (MAO) was used to prepare a Cu-doped TiO2 coating. The microstructure of the Cu-doped TiO2 coating was investigated by TF-XRD, SEM, and other characterization techniques, and its antifouling and anticorrosion properties were also tested. The results show the effects of the incorporation of Cu (~1.73 wt.%) into TiO2 to form a Cu-doped TiO2, namely, a Ti-Cu coating. The porosity (~4.8%) and average pore size (~0.42 μm) of the Ti-Cu coating are smaller than the porosity (~5.6%) and average pore size (~0.66 μm) of Ti-blank coating. In addition, there is a significant reduction in the amount of SRB adhesion on the Ti-Cu coating compared to the Ti-blank coating under the same conditions, while there is little difference in corrosion resistance between the two coatings. There, the addition of copper helps to improve the fouling resistance of TiO2 coatings without compromising their corrosion resistance. Our work provides a practical method to improve the antifouling function of metallic Ti substrates, which could promote the application of Ti in the petroleum industry.

Performance Evaluation of LoRa Communications in Harsh Industrial Environments

LoRa technology is being integrated into industrial applications as part of Industry 4.0 owing to its longer range and low power consumption. However, noise, interference, and the fading effect all have a negative impact on LoRa performance in an industrial environment, necessitating solutions to ensure reliable communication. This paper evaluates and compares LoRa’s performance in terms of packet error rate (PER) with and without forward error correction (FEC) in an industrial environment. The impact of integrating an infinite impulse response (IIR) or finite impulse response (FIR) filter into the LoRa architecture is also evaluated. Simulations are carried out in MATLAB at 868 MHz with a bandwidth of 125 kHz and two spreading factors of 7 and 12. Many-to-one and one-to-many communication modes are considered, as are line of sight (LOS) and non-line of Sight (NLOS) conditions. Simulation results show that, compared to an environment with additive white Gaussian noise (AWGN), LoRa technology suffers a significant degradation of its PER performance in industrial environments. Nevertheless, the use of forward error correction (FEC) contributes positively to offsetting this decline. Depending on the configuration and architecture examined, the gain in signal-to-noise ratio (SNR) using a 4/8 coding ratio ranges from 7 dB to 11 dB. Integrating IIR or FIR filters also boosts performance, with additional SNR gains ranging from 2 dB to 6 dB, depending on the simulation parameters.

Autonomous Scheduling for Reliable Transmissions in Industrial Wireless Sensor Networks

Deploying Internet of Things (IoT) on low-power lossy wireless sensor/actuator networks (LLN) in harsh industrial environments presents challenges such as dynamic link qualities due to noise, signal attenuations and spurious interferences. However, the critical demand for industrial applications is reliability of data delivery on low-cost low-power sensor/actuator devices. To address these issues, this paper proposes a fully autonomous scheduling approach, called Auto-Sched, which ensures reliability of data delivery for both downlink and uplink traffic scheduling and enhances network robustness against node/link failures. To ensure reliability, Auto-Sched assigns retransmission time slots based on the reliability constraints of the communication link. To avoid collision issues, Auto-Sched creates an upward pipeline-like communication schedule for uplink end-to-end data delivery, and a downward pipeline-like communication schedule for downlink scheduling. For enhancing network robustness, we propose a simple algorithm for real-time autonomous schedule reconstruction, when node or link failures occur, with minimal influence on communication overhead. Performance evaluations quantified the performance of our proposed approaches under a variety of scenarios comparing them with existing approaches.

Synthesis of block copolymer with cis-1,4-polybutadiene and isotactic-rich polystyrene using α-diimine nickel catalysts.

A series of styrene-butadiene di-block copolymers with high cis-1,4 unit content (greater than 92%) polybutadiene (PB) and isotactic-rich polystyrene (PS) (mmmm > 65%) was synthesized using α-diimine nickel catalysts (Ni-diimine). Four different Ni-diimine catalysts were synthesized via a complexing reaction between nickel (II) naphthenate and laboratory-made α-diimine ligands L1, L2, L3 and L4, which have different steric volume structures. The results indicate that the Ni-diimine catalyst prepared using the L4 ligand with a higher steric volume can effectively initiate the block polymerization of butadiene and styrene, and the resulting polymer has distinguished cis-1,4 structure unit PB and high isotactic-selective PS block. Differential scanning calorimetry and electrochemical performance tests show that these block copolymers with cis-1,4-regulated and isotactic-selective polymerization have advantages in terms of high-temperature and low-temperature resistance as well as corrosion resistance. Therefore, these copolymers are expected to be widely used in some harsh industrial environments.

Bench-scale processing of TiBOx/h-BN ceramics for alkaline water splitting applications

ABSTRACT: The successful development of novel materials for widespread alkaline water splitting has the potential to significantly reduce costs and accelerate industrialization. In this study, the commercialized materials ceramic structure TiBOx/h-BN was processed with a bench-scale facility, reaching the 480 mV overpotential at the current density of 10 mA cm−2 in oxygen evolution reaction (OER). Notably, this modified structure demonstrated stability in harsh industrial environments (6 M KOH, 60 °C), maintaining a current density of 580 mA cm−2 for 640 h, which suggests its potential to replace existing nickel-based anode materials. During the laboratory modification process, we selectively leached the OER unfavorable structures in the multicomponent ceramics based on the excellent properties of the narrow bandgap of the conductive ceramic bulk. As a result, an interfacial amorphous structure and a highly conductive bulk that synergistically achieves high OER performance were formed. The effectiveness of this method was corroborated by both X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS) results. This study provides a new strategy for developing stable and cost-effective OER materials for industrial alkaline water electrolysis (AWE) applications.

A novel abnormal data detection method based on dynamic adaptive local outlier factor for the vibration signals of rotating parts

Abnormal signals are inevitable in big data acquired from harsh industrial environments. Abnormal data detection is a crucial component of condition monitoring for rotating parts and is also the premise of data cleaning, compensation, and mining. To detect abnormal data segments of rolling bearings, this paper proposes a dynamic adaptive local outlier factor (DALOF) anomaly detection method. First, a data dynamic segmentation method based on sliding windows is designed to determine samples with variable lengths. Then, a time-domain feature extraction and fusion method based on principal component analysis is exploited to reduce the feature space discrepancy. To improve the accuracy of abnormal data detection, a data quality evaluation model is established to assess each data segment using DALOF. The validity of the proposed method is also verified by analyzing signals including missing data, random interference data, and drift data. Several other methods are respectively applied to identify these abnormal data to further demonstrate the benefits of the developed methodology.

Microencapsulated paraffin as a tribological additive for advanced polymeric coatings

Numerous tribological applications, wherein the use of liquid lubricants is infeasible, require adequate dry lubrication. Despite the use of polymers as an effective solution for dry sliding tribological applications, their poor wear resistance prevents the utilization in harsh industrial environment. Different methods are typically implemented to tackle the poor wear performance of polymers, however sacrificing some of their mechanical/tribological properties. Herein, we discussed the introduction of a novel additive, namely microencapsulated phase change material (MPCM) into an advanced polymeric coating. Specifically, paraffin was encapsulated into melamine-based resin, and the capsules were dispersed in an aromatic thermosetting co-polyester (ATSP) coating. We found that the MPCM-filled composite exhibited a unique tribological behavior, manifested as “zero wear”, and a super-low coefficient of friction (COF) of 0.05. The developed composite outperformed the state-of-the-art polytetrafluoroethylene (PTFE)-filled coatings, under the experimental conditions examined herein.

Adaptive evolution of microorganisms based on industrial environmental perturbations

The development of synthetic biology has greatly promoted the construction of microbial cell factories, providing an important strategy for green and efficient chemical production. However, the bottleneck of poor tolerance to harsh industrial environments has become the key factor hampering the productivity of microbial cells. Adaptive evolution is an important method to domesticate microorganisms for a certain period by applying targeted selection pressure to obtain desired phenotypic or physiological properties that are adapted to a specific environment. Recently, with the development of technologies such as microfluidics, biosensors, and omics analysis, adaptive evolution has laid the foundation for efficient productivity of microbial cell factories. Herein, we discuss the key technologies of adaptive evolution and their important applications in improvement of environmental tolerance and production efficiency of microbial cell factories. Moreover, we looked forward to the prospects of adaptive evolution to realize industrial production by microbial cell factories.

Inverse-GMM: A Latency Distribution Shaping Method for Industrial Cooperative Deep Learning Systems

The front deployed deep learning is a promising technology of the next generation industrial applications, which can extract essential information from high dimension sensors. However, part of these heavy computation tasks at resource constrained front devices have to be offloaded to the edge or cloud devices, which forms the cooperative deep learning system through the exchange of intermediate data. The inference efficiency of cooperative deep learning system will then be highly correlated with the communication latency caused by the non-stationary industrial multipath-rich fading channel. This paper proposes a novel method to control the distribution of communications latency, which is able to support efficient cooperative deep learning architecture in the harsh industrial environment. The proposed method is essentially an inverse process of Gaussian Mixture Model (GMM), which adjusts latency samples to approach the given arbitrary shape function. To achieve this objective, a new variation of Expectation-Maximization (EM) algorithm in analytical domain is derived to decompose arbitrary distribution shape with multiple Gaussian kernels and an optimized stochastic resource allocation algorithm is proposed to approximate each Gaussian kernels. The performance of proposed method is verified by both classical Rician channel model and field measured industrial fading channel responses.

A Delay-Sensitive Multibase-Station Multichannel Access System for Smart Factory

The intelligence of industrial production depends on real-time and reliable transmission and timely feedback of information in the Industrial Internet of Things (IIoT) systems. However, the scarcity of spectrum resources, a harsh industrial electromagnetic environment, and a need for real-time processing of massive traffic have posed severe challenges to the real-time and reliable access of the MAC layer. Aiming to solve the real-time transmission problem in the IIoT system, a delay-sensitive multibase-station multichannel access scheme is proposed. First, a multibase-station multichannel access model for real-time transmission is developed. The way to match sensing nodes with base stations and channels is formulated as a delay-sensitive multibase-station multichannel access (DMMA) problem. Moreover, it is theoretically proved that the DMMA problem is NP-Hard. Finally, a delay-sensitive greedy schedule (DG-Schedule) scheme based on the minimum deadline priority is proposed to obtain a feasible solution to the DMMA problem. According to experiments on the TI CC2530, DG-Schedule can reduce packet loss by 11% and average latency by 88% compared to CSMA/CA. Therefore, the DG-Schedule scheme is more suitable for IIoT scenarios.

Autonomous Traffic-Aware Scheduling for Industrial Wireless Sensor-Actuator Networks

Recent years have witnessed rapid adoption of low-power Wireless Sensor-Actuator Networks (WSANs) in process industries. To meet the critical demand for reliable and real-time communication in harsh industrial environments, the industrial WSAN standards make a set of specific design choices, such as employing the Time-Slotted Channel Hopping (TSCH) technique. Such design choices distinguish industrial WSANs from traditional Wireless Sensor Networks, which were designed for best-effort services. Recently, there has been increasing interest in developing new methods to enable autonomous transmission scheduling for industrial WSANs that run TSCH and the Routing Protocol for Low-Power and Lossy Networks (RPL). Our study shows that the current approaches fail to consider the traffic loads of different devices when assigning time slots and channels, which significantly compromises network performance when facing high data rates. In this article, we introduce a novel Autonomous Traffic-Aware transmission scheduling method for industrial WSANs. The device that runs ATRIA can detect its traffic load based on its local routing information and then schedule its transmissions accordingly without the need to exchange information with neighboring devices. Experimental results show that ATRIA provides significantly higher end-to-end network reliability and lower end-to-end latency without introducing additional overhead compared with a state-of-the-art baseline.

Powering a Low Power Wireless Sensor in a Harsh Industrial Environment: Energy Recovery with a Thermoelectric Generator and Storage on Supercapacitors

Powering a Low Power Wireless Sensor in a Harsh Industrial Environment: Energy Recovery with a Thermoelectric Generator and Storage on Supercapacitors

Adaptive Edge Sensing for Industrial IoT Systems: Estimation Task Offloading and Sensor Scheduling

Edge sensing can achieve high-performance state estimation in industrial IoT systems by supporting task offloading and data processing at powerful edge estimators. Accurate edge sensing depends on low offloading delay. However, it is challenging to decrease offloading delay due to the harsh industrial environment and limited communication-and-computation resources. In this article, a closed-form expressing of estimation error with respect to offloading delay is derived to indicate that adjusting offload delay on demand is necessary for estimation error reduction. Then, we propose an adaptive edge sensing scheme, aiming to minimize estimation error by jointly optimizing task offloading and sensor scheduling. The required optimization is formulated as a mixed-integer nonlinear programming problem and solved by the designed decomposition and approximation methods. Specifically, the maximum matching is used for sensor scheduling to assign the optimal edge estimator for each sensor. The task offloading algorithm is designed based on the inner approximation method to reduce the offloading delay. Finally, simulation results demonstrate that the proposed scheme has superiorities in reducing estimation error compared with centralized sensing and distributed sensing schemes. Moreover, we find an interesting result that estimation error is delay sensitive when the offloading delay is large.

Speech recognition and enhancement in underground mines for the use of smart voice assistants

Smart voice assistants are being used progressively in new fields and environments. This trend has also reached Polish underground mines, where the first attempts to implement solutions of this type are already being executed. Unfortunately, the underground mine environment places great demands on the operation of such assistants. This problem is even greater when we take into account less popular and more difficult languages like Polish. In this article, we approach the problem of noise in the underground mine environment and the correct transmission of speech to text by a smart voice assistant. In particular, we researched the possibility of using various algorithms for speech enhancement. This was achieved by taking a series of recordings in an underground environment, during which a voice assistant was used in a standardized way by different people. This voice material was then denoised using 10 different algorithms, converted to text, and evaluated. Such a comparison showed that only one of the chosen methods influenced the speech-to-text solution in a positive way, showing that current algorithms are not sufficient for harsh industrial environments similar to underground mines.

Erosion-resistant materials demonstrate low interfacial toughness with ice and superior durability.

The strong adhesion of ice to surfaces results in unwanted effects in various industrial activities. However, current strategies for passive ice-phobic purposes lack either scalability or durability, or both, in industrial applications. In this study, erosion-resistant materials, including ceramic-based (WC, SiC, and alumina) and metal-based (a quasicrystalline coating, QC), were studied for their ice-phobic properties via push-off tests with bulk-water ice from -5 to -20 °C. Although their ice adhesion strengths were high (>400 kPa), their interfacial toughness with ice was quite low (1.1 to 2.6 J m-2) and comparable to polymeric surfaces. The force per width required to remove ice on the QC surface was even lower than that of a silicone (Sylgard 184) surface for an ice length of 7.0 cm. The low interfacial toughness of the erosion-resistant materials with ice was also retained after 1000 cycles of linear abrasion under a pressure of 27.0 kPa. The findings of this work expand the material selection options for durable large-scale ice-phobic applications and could enlighten the use of erosion-resistant materials in harsh industrial environments requiring effective de-icing.