Industrial Operations Research Articles

The impact of Block chain-Based System on Goods Tracking and management in Industrial Environment

In the domain of Industrial Internet of Things (IIoT), the demand for robust and secure methods for goods tracking and management has become growingly critical. Conventional methods face significant challenges, including authentication, computational overhead, cyber security, and data integrity. To address these issues, this paper proposes a block-chain based system for goods management and tracking with enhanced authentication mechanism by leveraging the decentralized nature of block-chain technology and integrating Elliptic Curve Digital Signature Algorithm (ECDSA) with Elliptic Curve Cryptography-Zero Knowledge Proof (ECC-ZKP). The proposed solution aims to ensure the authenticity and the integrity for all the transaction while providing high level privacy-preserving verification without revealing information. The research in this paper demonstrates that the proposed block-chain-based system significantly enhances security performance, key management and operational efficiency, addressing the existing challenges in IIoT goods tracking and management, providing a resilient framework for more secure industrial operations in managing goods.

Integrated Framework for Industrial Internet of Things Authentication Leveraging Block chain, Proxy Re-encryption, and AI Techniques

The digital evolution has fostered the rise of the Internet of Things (IoT) and its industrial counterpart, the Industrial Internet of Things (IIoT). While IoT is used to connect everyday devices for daily use, IIoT is responsible on optimizing the industrial processes. This paper first explores blockchain-enhanced authentication mechanisms in industrial settings, highlighting their benefits, challenges, and practical applications. In response to the challenges faced, we propose a novel framework that integrates and enhances upon the leading solutions. Our proposed solution is integrated with block chain and RFID technologies, proxy re-encryption, and a block chain authentication mechanism empowered with Transfer Learning (TL). This integrated approach aims to enhance security, efficiency, and adaptability in IIoT environments. Through comprehensive analysis, the paper aims to provide in-depth review into the potential applications and future directions of blockchain-enabled authentication methods in IIoT, contributing to the advancement of secure and efficient industrial operations.

Creation of an identifier database for the ammonia synthesis computer control system

The object of research is the control system of the synthesis department of the large-tonnage ammonia production unit of the AM-1360 series. An analysis of the functioning conditions of the synthesis department was carried out. The significant influence of the content of inerts in the synthesis cycle on the energy efficiency of ammonia production is shown, the optimal value of which depends both on the accepted level of prices for energy carriers and on the method of using purge gases. The need to create a computer-integrated control technology to optimize the use of purge gases is established. The function of the identifier of such a control technology is defined, namely, continuous refinement based on current information about the state of the main sections of the synthesis department, such as the synthesis column, primary and secondary condensation. The operation of these stations takes place in the conditions of seasonal and daily changes in the heat load, which causes parametric uncertainty of such parameters of the connection of the mathematical model as the concentration of ammonia in the circulating gas at the outlet of the stations listed above. Numerical assessment of these uncertainties according to the results of analytical studies for such technological objects is most often performed using stochastic approximation methods. Let’s note that the significant inertia of the objects of the synthesis department under certain conditions makes it impossible to adapt the parameter to its actual value. An algorithmic base has been created for the formation of an information array of the identifier of the computer-integrated control technology of the ammonia synthesis department, which ensures the separation of transient modes under conditions of uncertainty and allows to perform the task of identifying processes in the synthesis column, in the primary and secondary condensation units. The proposed algorithm allows to perform convergence in the department both on the general and on the component material balances. The algorithm is implemented in the MatLab application program package and tested by means of simulation based on experimental data of industrial operation of AM-1360 series ammonia synthesis units.

Modeling the Dynamic Loads Affecting a Bridge Crane during Start-Up

Introduction. The dynamic loads during the start-up of a bridge crane can cause excessive stress in the structure and components, leading to potential safety hazards and increased wear and tear. To reduce the influence of the dynamic loads, various strategies can be implemented including optimization of the acceleration and deceleration profiles, using the soft start controls, implementing the vibration damping systems. It is vital to ensure that the proper crane maintenance and inspection protocols are in place. By reducing the impact of dynamic loads during the start-up, the overall performance and longevity of a bridge crane can be improved, ultimately enhancing safety and efficiency of the industrial operations. The present research offers a new approach to improving the efficiency and safety of industrial operations by providing a more precise account of the dynamic loads during the start-up of a bridge crane. The objective of this study is to develop a mathematical model for investigating the mechanical properties of the bridge cranes by analyzing the dynamic loads that occur during lifting operations.Materials and Methods. The development of the mathematical model was based on the kinetic model of the system, which included three connecting blocks and two flexible connections for a more accurate description of the bridge crane structure. Lagrange’s equations incorporating the information about the geometry and structure of a bridge crane were used. They made it possible to describe the motion of a system with the multiple elements and degrees of freedom. Processing and analysis of the results of the mathematical model were carried out in the MATLAB program using the Runge-Kutta method.Results. As a result of the research, a mathematical model was developed to study the dynamic loads affecting a bridge crane during lifting operations. Graphs describing the dependences of speed, acceleration, load, and rope angle over time, and their influence on the crane beam were plotted. The changes in these parameters over time, including their maximum values, were analyzed. The reasons for load changes and factors influencing the extension of lifting machines’ service life as well as reducing metal consumption during production thereof were identified.Discussion and Conclusion. The developed mathematical model and its numerical solution using the specialized software (MATLAB) allow for conducting the dynamic analysis of the bridge crane structures and determining the optimal design solutions. The analysis of the factors influencing the load changes leads to the conclusion that the use of this model can significantly reduce the load magnitudes and metal consumption, as well as increase the service life of lifting machines. The results obtained with the developed mathematical model and its numerical solution are useful for optimizing the crane structures, providing compliance with the operational requirements, and extending the service life of lifting machines.

Power grid network security: A lightweight detection model for composite false data injection attacks using spatiotemporal features

The stability of power systems is paramount to industrial operations. The deleterious inherent characteristics of false data injection attacks (FDIA) have drawn substantial interest due to their severe threats to power grids. Contemporary detection systems face numerous challenges as attackers employ various tactics, such as injecting complex elements into measurement data and formulating quick attack strategies against critical nodes and transmission lines in the power grid network topology. Conventional models often fail to adapt to the intricacies of practical situations because they focus predominantly on detecting individual components. To overcome the above predicaments, this paper proposes a lightweight detection model integrating deep separable convolutional layers, squeeze neural networks, and a bidirectional long short-term memory architecture named DSE-BiLSTM. The acquisition process of network topological characteristics is accomplished through variable graph attention autoencoder (VGAAE). This approach leverages the effectiveness of the graph convolution (GCN) layer to acquire each node’s topological feature and the graph attention (GAT) module to identify and extract the topological features of critical nodes. Furthermore, the topology information obtained by the both techniques is embedded in one-dimensional vector space in the same form as measurement data. By combining the output of VGAAE with meter measurements, the feature fusion of temporal and spatial modalities is realized. DSE-BiLSTM with optimal hyperparameters achieves an F1-score of 99.56% and a row accuracy (RACC) of 93.10% on the conventional dataset. The experimental results of FDIA detection with composite datasets of IEEE 14-bus and IEEE 118-bus systems show that the F1-score and RACC of DSE-BiLSTM remain above 84.51% and 83.56% under various attack strengths and noise levels. In addition, as the power grid network scales up, noise level’s effect on detection performance decreases, while attack strength’s effect on recognition capability increases. DSE-BiLSTM can effectively process the composite data of spatiotemporal multimodes and provides a feasible solution for the localization and detection of FDIA in realistic scenes.

A review of the status of air quality monitoring in the Permian Basin, USA, and its implications for effective long-term monitoring of industrial operations

The Permian Basin is the largest petroleum producing region in the United States. Few studies have investigated impact the Petroleum Industry had on the air quality of the Permian Basin. Industry operations in the Basin has two primary impacts on air quality, (a) it significantly contributes carbon dioxide (CO2) and methane (CH4) to the atmosphere during extraction, transport and use of fossil fuels, (b) industry operations influence public health especially when populations are in proximity to all categories of oil and gas facilities. The advent of Covid-19 in late 2019 and a severe drought that began prior to 2019 in West Texas have created unusual circumstances regarding air quality and public health in the Permian Basin. This study has monitored airborne pollen, spores, fungal spores, and other organically derived particles, and inorganic particulate matter (1, 2.5, 10-micron particulate matter (PM) and total suspended particles (TSP)), volatile organic compounds (VOCs), and sulfur dioxide (SO2). The pandemic caused a historic reduction in oil and gas production slowing operations in the Permian Basin to near zero during the study period, reducing environmental effects on air quality and concomitantly an increase in flaring of natural gas. Although there were increases in the release of VOCs fixed base monitors detected no notable increase. However, an increase in the concentration of airborne pollutants (VOCs and SO2) shows a close correlation with proximity to oil and gas facilities and the intensity of industry operations. This phenomenon is attributed to the highly local nature of flaring and CH4 plume emissions from leaking oil and gas facilities. Air quality improved in the Permian Basin due to reduction in production activity because of the Covid-19 pandemic, and the reduction of particulate matter due to the ongoing drought during the study period.

Green Lean Six Sigma in the food industry: a systematic literature review

PurposeThe current paper aims to discuss the implementation of Green Lean Six Sigma (GLSS) in the food industry to improve sustainable practices. The focus is more specifically on dairy processors to ascertain the current state of the literature and aid future research direction.Design/methodology/approachUtilising a systematic literature review (SLR), the paper addresses various terms and different written forms in the literature. The study characterises the current deployment of GLSS in the food industry and explains the reported benefits of this approach.FindingsGLSS, a concept that has yet to be fully explored in the food industry, as in other sectors, holds significant potential to enhance the food industry’s sustainability practices. The dairy sector, a subsector of the food industry known for its high greenhouse gas emissions, is a prime candidate for the application of GLSS. In instances where it has been applied, GLSS has demonstrated its effectiveness in improving sustainability, reducing waste, lowering greenhouse gas emissions and minimising water usage. However, the specific tools used and the model for GLSS implementation are areas that require further study, as they have the potential to revolutionise food industry operations and reduce their environmental impacts.Practical implicationsBenchmarking of this research by the food industry sector and by academics can aid understanding of the practical application of GLSS tools and aid implementation of these practices to evolve the dairy processing sector in the next decade as sustainability champions in the sector.Originality/valueThis study extensively analyses GLSS in the food industry, with a particular focus on dairy processors.

Fast and nondestructive discrimination of coal types based on spectral feature parameters

Coal type identification is the basic work of coal quality inspection, which is of great significance to the normal operation of power generation, metallurgy, and other industries. The traditional coal-type identification method is complicated and requires comprehensive determination of various chemical parameters to obtain more accurate analysis results. Hyperspectral detection and analysis technology has the advantages of being simple, fast, nondestructive, and safe, and is widely used in a variety of fields. In this study, typical spectral feature parameters of coal samples were extracted based on hyperspectral data, and the parameters' sensitivity to coal types was explored using one-way ANOVA. The results showed that the coal spectral feature parameters of DI1-2μm and AD2.2μm significantly differed with coal species, indicating that the two parameters were class-sensitive features. When DI1-2μm and AD2.2μm were used to construct the Fisher discriminant model, the coal types could be discriminated with high accuracy. At the same time, the correlation between the extracted spectral feature parameters and the physicochemical parameters of bituminous coal and anthracite was analyzed. The results showed that there was a certain basis for using the extracted spectral feature parameters as the sensitive spectral characteristics of the model, and the application potential of the spectral characteristics of coal in the nondestructive prediction analysis of coal parameters was further discussed.

Is Pharmaceutical Marketing Ethical?

The pharmaceutical industry is expanding, and there are many new scientific breakthroughs in the modern day. As a result, moral concerns sparked by marketing strategies have led to intense global debates. Pharmacies are trying to market their products and maintain their competitiveness, raising concerns about potential conflicts between corporate interests and the fundamentals of medical ethics. This article takes a multidisciplinary approach, incorporating public health, bioethics, and regulatory frameworks to begin a thorough investigation of the complex ethical landscape surrounding pharmaceutical marketing. The background section shows how pharmaceutical marketing methods have evolved by closely examining empirical evidence and ethical perspectives. It demonstrates how the significant move toward direct-to- consumer advertising (DTCA) has altered the healthcare industry's operations. To illustrate how intricate modern marketing strategies are, the article closely examines various techniques, including sponsorships, patient assistance programs, physician detailing, and promotional materials. The piece's primary focus is its in-depth analysis of ethics, founded on the fundamental ideas of beneficence, justice, non-maleficence, and respect for individual liberty. Pharmaceutical marketing has been scrutinized ethically for an extended period. Opponents argue that practices such as advertising and compensating doctors to promote specific treatments are considered to breach medical ethics. Advocates argue that the current methods are beneficial for education and can be ethically utilized with adequate regulation. This study examines the various perspectives in the ongoing debate. Critics argue that drug marketing results in an excessive number of prescriptions for unnecessary medications. False information about diseases is circulated to create the perception that common illnesses require pharmacological treatment. Studies indicate that exposure to marketing is associated with increased prescription rates and expenses, with no apparent health advantages for the general population. Gifts and incentives provided to doctors for marketing purposes are viewed as manipulative and compromising the impartial examination of facts. Detailers advocate for expedited utilization of new medications before their efficacy and safety profiles are fully understood. Some argue that direct-to-consumer advertising prioritizes patients' desires over doctors' professional judgment. High marketing expenses, which are transferred to prescription pricing, create obstacles for individuals to get essential medications. Associates view marketing as a way to raise awareness for overlooked disorders and educate doctors about novel therapies. Advocates of marketing argue that false advertising is against the law and that physicians rely mostly on medical research when utilizing promotions. According to polls, numerous doctors believe that marketing does not influence them. Supporters also cite evidence indicating that patients are adhering more strictly to healthcare norms, and they argue that warnings provide patients with sufficient information to make informed judgments. Profits are used to fund future research projects that must be commercialized before the patent expires. Both parties utilize substantial evidence while also depending on assumptions regarding impacts. Critics argue that the objectives of marketing are inherently unethical in contrast to evidence-based prescribing. Advocates argue that with careful supervision, appropriate utilization is achievable, albeit adjustments may be necessary. There is limited research demonstrating the definitive effects of something as either harmful or beneficial. Choosing the correct strategy remains a challenging process that requires making compromises. Creating ethically good norms requires meticulous effort to prevent misconduct while also allowing for the dissemination of knowledge. This problem warrants a thorough examination from both an empirical and normative perspective.

Конкурентоспроможність туристичних дестинацій в умовах сучасних викликів

Modern tourist destinations in the world are a driver of the socio-economic development of territories, exerting a positive influence on the growth rate of the gross regional product, increasing profits, and the development of adjacent branches of the national economy, closely related to tourism. The need for accelerated development of the tourism industry makes it necessary to solve the problem of increasing the competitiveness of the national tourist product in both domestic and foreign markets. Such a task is especially relevant now, as most tourist destinations face fierce competition. The significance of this publication lies in its exploration of the competitiveness of tourist destinations, the identification of factors that shape their competitive advantages, and the proposal of strategies to enhance their development. This research is not just informative, but it is also crucial for understanding the dynamics of the tourism industry and for guiding future strategies in this field. The article examines the essence and nature of the popularity of tourism. Tourism development was determined to affect enterprises’ successful operations in the tourism industry and related industries. The Tourism Industry Development Index was studied, and specific global trends such as digitalization, sustainability, and changing consumer preferences affecting the tourism industry were determined. The essence of the tourist destination is substantiated, and it is stated that competitiveness is necessary for its stable and prosperous development. According to the evaluations of various scientific studies, the factors that form competitive advantages and the directions of their creation have been determined. It is justified that the destination’s competitiveness implies safety, efficiency, quality, high productivity, adaptability, success, modern management, excellent products, and low costs. The competitiveness of tourist destinations can be significantly enhanced through the efficient and rational use of environmental, social, and cultural resources. This research not only provides valuable insights into the competitive strategies but also equips the reader with actionable knowledge that can be adopted to elevate the status of a territory in the hospitality sector. Keywords: tourism, tourist destination, competitive advantages, competitiveness, socio-economic development.

Exploration on Task-driven Teaching Design of Industrial Robot Operation and Programming Course in Secondary Vocational Schools

Currently, the national goal for the training of talents in vocational colleges and universities is to cultivate high-quality skilled talents with powerful practical ability, good thinking, and in line with the needs of the manufacturing industry. For the industrial robot operation and programming, which is a comprehensive practical training course with both theory and practice, focusing on the improvement of students' Introductory operation, offline programming ability, and familiarity with the use of network communication systems and other learning objectives, it is proposed to apply the "task-driven teaching method" throughout the whole teaching process to fully mobilize students' initiative and guide them to analyze the problems and improve their practical skills from different perspectives. The article focuses on the requirements of the course and discusses how to improve the student's capacity to apply theory in practice. The article focuses on the specific teaching design ideas of the task-driven teaching method and the multi-dimensional evaluation and assessment mode in this course in combination with the requirements of the course.

Validation of Sensor Data Integrity in OT Environments Through Multisource Data Sensors

This research paper focuses on detecting cyber threats from the OT environment by combining data from multiple sources. Monitoring cyber security or hybrid threats in an industrial OT environment is difficult due to different equipment, protocols, environments, personnel management and training, etc. However, the OT environment can also be observed with a multisource sensor system, which can be used to collect data. By combining IT and OT data, additional cyber threats can be found. Especially concerning the integrity of OT command-and-control data. We deal with the key concepts and differences of the industrial operating environment, which create challenges compared to the traditional IT environment. This is important because the policies defined at the European level for the NIS2 regulation are coming to touch all member countries, regardless of what the national implementation schedule is. The increased standards for OT environment cyber security implementation and development will also have an impact on the personnel management and training to support the onboarding of the standards in practice. Critical infrastructure protection is important because, without the protection of critical infrastructure, vital functions cease to function. Hostile actors cause security challenges among Western actors. In this study, we delve into whether it is possible to find threats concerning OT command-and-control process. The increased data surface collected from the IT/OT environment improves the capabilities for the system to detect malicious attacks towards the OT system. With the help of test equipment, the goal is to demonstrate that it is possible to find threats by combining data from multiple sources. With the help of test equipment, we find out IT and OT capabilities, which we load with various attacks and anomalies. We produce added value compared to traditional monitoring method test cases by comparing data obtained from different sources. The research paper shows the importance of detecting OT threats. By monitoring IT and OT environments and combining their data, we can find hidden threats. Only one test equipment configuration has been used in the study, but the results can be generalized and classified. The study also provides guidelines for how the detection of cyber threat capabilities should be developed.

Improving Detection Capabilities in OT Environments Through Multisource Data Sensors

This research focuses on implementing cyber threat detection in OT environments by combining data from IT and OT sensors and logs to enhance SOC's situational awareness. OT environment is challenging to monitor and includes various sensors. We deal with the key concepts and differences of the industrial operating environment, which create challenges compared to the traditional IT environment. This is important because the policies defined at the European level for the NIS2 regulation will affect all member countries. Hostile actors cause security challenges highlighting the importance of critical infrastructure protection. Cyber security solutions have often solely focused on IT threats, but similar investments have yet to be made in response to the challenges of the OT environment. The security solutions of OT operators rely heavily on solutions from the IT side. Here, we delve into whether it is possible to find threats in the IT/OT ecosystem by combining data from the IT and OT sides. All threats are not found by monitoring data separately from IT or OT sources but we identified hidden threats by monitoring and comparing IT and OT data. This paper shows the importance of detecting OT threats. The study proposes how the detection of cyber threat capabilities should be developed.

Determination of Contaminant Transport Parameters for a Local Aquifer by Numerical Modeling of Two Plumes: Trichloroethylene and Hexavalent Chromium

The municipal wellfield in Collierville, Tennessee, is contaminated with trichloroethylene (TCE) and hexavalent chromium (Cr (VI)) due to industrial operations dating back to the 1970s and 1980s. This study aims to elucidate the aquifer’s contaminant transport mechanisms by determining longitudinal and transverse dispersivities through inverse modeling. Utilizing MT3DMS for contaminant transport simulation, based on a well-calibrated groundwater flow model, and leveraging Python’s multiprocessing library for efficiency, the study employs a trial-and-error methodology. Key findings reveal that longitudinal dispersivity values range from 5.5 m near the source to 20.5 m further away, with horizontal and vertical transverse dispersivities between 0.28 m and 3.88 m and between 0.03 m and 0.08 m, respectively. These insights into the aquifer’s dispersivity coefficients, which reflect the scale-dependent nature of longitudinal dispersivity, are crucial for optimizing remediation strategies and achieving cleanup goals. This study underscores the importance of accurate parameter estimation in contaminant transport modeling and contributes to a better understanding of contaminant dynamics in the Collierville wellfield.

Analysis of Raw Material Inventory Control with A Tabular Approach and Formula Approach Economic Order Quantity (EOQ) to Optimize The Cost of Soybean Raw Material Inventory At The Rizqy Putra Fried Tofu Factory

Inventory management is a crucial aspect in the operations of the manufacturing industry, including in fried tofu factories such as the Rizqy Putra Fried Tofu Factory. The main raw material, namely soybeans, must be managed efficiently to ensure smooth production and minimize the costs incurred. This research aims to determine the inventory control of soybean raw materials applied by Rizqy Putra Fried Tofu Factory, the total inventory cost, and the optimal quantity of soybean raw material orders. This research is quantitative research with a comparative descriptive analysis. The data processed are the inventory purchase and usage reports of soybean raw materials at Rizqy Putra Fried Tofu Factory in 2023. The data analysis method used in this research includes two approaches: the tabular approach and the formula approach with the calculation technique of Economic Order Quantity (EOQ). Based on the results of the research, the inventory control method applied by Rizqy Putra Fried Tofu Factory is the traditional or conventional method, where the total inventory cost of soybean raw materials is Rp. 18,720,000 with details of storage costs amounting to Rp. 18,000,000 and ordering costs of Rp. 720,000 per year, and an ordering frequency of 48 times with an average order quantity of 5,875 kg. Meanwhile, according to the calculation of the formula approach EOQ, the optimal order quantity is 2,880 kg, with an ordering frequency of 24 times and the total inventory cost incurred is Rp. 734,387. The conclusion of this research indicates that based on the calculation of the EOQ formula approach, the total inventory cost of soybean raw materials is more optimal compared to the conventional method applied by Rizqy Putra Fried Tofu Factory.

Nitrogen and Sulfur Oxides Emissions from Fuel Oil Combustion in Industrial Steel Reheat Furnace

Many industrial heating operations still use fueloil for combustion instead of the cleaner gaseous fuels. Inthe steel industry, large furnaces referred to as reheat furnaces are used to heat steel stocks to rolling temperaturesusing normally fuel oil for combustion. The combustionproducts of flue gases consist of mainly carbon dioxide,water vapor and traced of other oxides such as nitrogen andsulfur oxides. These oxides are of a concern to theenvironment and should be minimized as much as possible.Experiments were carried out on a steel billet reheatfurnace in which air/fuel ratios are varied while the nitrogenoxide content is being monitored using furnace instruments.These variations in air/fuel ratios for combustion are foundto affect the concentration of nitrogen oxides in the flue gaswhere higher air/fuel ratios promoted the formation ofhigher nitrogen oxides during combustion. The sulfurdioxide content in the flue gas was calculated based onactual measurements of air and fuel rates to the furnace andfound to be minimal in the flue gas.

Experimental study on ammonia-diesel co-combustion in a dual-fuel compression ignition engine

Ammonia, as a carbon-free renewable fuel and a potential hydrogen carrier, is gaining increasing interest in addressing greenhouse gas emissions in industrial applications and transportation. This paper presents experimental studies on the co-combustion of ammonia with diesel fuel in a single-cylinder industrial compression ignition engine operating in dual-fuel mode. The energy share of ammonia ranged from 0 to 60%. Increasing the proportion of ammonia compared to diesel fuel in the compression ignition engine leads to a change in the combustion process character, increasing the kinetic combustion phase at the expense of diffusive combustion. However, it does not result in an increase in the peak pressure rise (PPR) beyond the permissible value of 1 MPa/deg for piston engines. It increases ignition delay and shortens the combustion duration, while also increasing thermal efficiency and decreasing specific energy consumption. An increase in the NH3 share results in a decrease in combustion efficiency, a reduction in unit emissions of NO, CO2, and soot, and an increase in CO emissions. Beyond 42% ammonia share, it reduces unit THC emissions. At a 42% ammonia share, the dual-fuel engine achieves the highest indicated thermal efficiency (ITE), lowest specific energy consumption (SEC), shortest combustion duration, and lowest unit soot emissions.

A Computational Framework for Enhancing Industrial Operations and Electric Network Management: A Case Study

A Computational Framework for Enhancing Industrial Operations and Electric Network Management: A Case Study

Macroplastic and Microparticle Pollution in Beach Sediments from Urias Coastal Lagoon (Northwest Mexico).

This study investigates the occurrence and characteristics of macroplastic and polymer microparticles in the Urias coastal lagoon's beach sediments, in northwest Mexico. Coastal lagoons, productive and vulnerable ecosystems, are impacted significantly by anthropogenic activities, leadings to their pollution by various contaminants, including plastics. Our research involved sampling sediments from four sites within the lagoon that were influenced by different human activities such as fishing, aquaculture, thermoelectric power plant operations, industrial operations, and domestic wastewater discharge. Our methodology included collecting macroplastics and beach sediment samples, followed by laboratory analyses to identify the plastic debris' size, shape, color, and chemical composition. The results indicated a notable presence of macroplastic items (144), predominantly bags, styrofoam, and caps made of polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET). The polymer microparticles were mainly fibers, with cotton and polyester as the most common polymers, suggesting a significant contribution from clothing-related waste. The dominant colors of the microparticles were blue and transparent. High densities were observed in areas with slower water exchange. Our findings highlight the urgent need for better waste management practices to mitigate plastic pollution in coastal lagoons, preserving their ecological and economic functions.

A fast data-driven fault detection and location method for unknown distributed thermal processes

The distributed thermal processes are widely present in various industrial operations, but the presence of time delay effect and unknown model information make achieving fast and timely fault detection challenging. Furthermore, the involvement of spatiotemporal coupled data in measurement processes further exacerbates the situation. To solve these problems, this paper proposes a data-driven fault detection and location method for thermal processes described by distributed parameter systems (DPS). Firstly, a Time/Space separation is employed to decoupling the spatio-temporal data into spatial basis functions and temporal coefficients. Subsequently, dynamic partial least squares (DPLS) is employed to obtain the temporal information of the model with fault-free data. Monitoring statistics in both the temporal and spatial domains are then constructed, and their thresholds are estimated by kernel density function. Finally, an online strategy for fault detection and location is presented. The method was validated on the catalytic rod, and an experimental snap curing oven.