Process Safety Research Articles

Cross-domain fault diagnosis for multimode green ammonia synthesis process based on DA-CycleGAN

Green ammonia is a crucial strategy for reducing carbon emissions and promoting sustainable development. However, in industrial applications, the production load of the ammonia synthesis section must be adjusted to accommodate fluctuations in renewable energy generation and hydrogen production. Consequently, the green ammonia synthesis process operates under multiple conditions with varying production loads. The operations of this multimode process introduce new challenges for process safety. Traditional fault diagnosis methods experience significant performance degradation when production conditions change. In new conditions, only a small number of normal samples can be obtained, and no fault samples are available. To address this issue, a novel transfer learning method named DA-CycleGAN is proposed for the multimode green ammonia synthesis process. This method combines a two-dimensional generation model based on CycleGAN (Cycle-Consistent Generative Adversarial Networks) with domain adaptation to enhance model performance in cross-domain tasks. The feasibility of the proposed method was initially validated using the benchmark Tennessee-Eastman process for fault diagnosis. Subsequently, a case study of the green ammonia synthesis process demonstrated that it significantly enhances performance in multimode processes, ensuring process safety and reducing losses for industrial applications.

Harnessing the hidden environmental power of Bjerkandera adusta laccase: Sustainable production, green immobilization, and eco-friendly decolorization of mixed azo dyes

This research unveils the untapped potential of laccase, derived from the Bjerkandera genus, utilizing it in an immobilized system aimed at detoxifying harmful azo dye effluents from the textile industry, thus contributing to environmental protection. Marking a pioneering achievement, we recorded the highest laccase activity at 94.52 U/g cultivating B. adusta's mycelium on brewer's spent grain, enhanced with lignocellulosic waste, under meticulously optimized conditions −2.69 g of alkali-pretreated beech sawdust, 0.91 g of cypress cone, and 8-day incubation period. The harvested laccase was subjected to an immobilization process on alkali-pretreated beech sawdust, where, through optimization, we established the ideal conditions (30 mg of carrier, pH 7, and 3.5 h), achieving an immobilization efficiency of 72.24% with a residual activity of 57.64%. Remarkably, both free and immobilized forms of the B. adusta TMF1 laccase enzyme demonstrated formidable efficacy in decolorizing a mix of three distinct azo dyes (Orange G, Eriochrome Black T, and Congo Red), eliminating over 63% of the coloration within just 30 min. The immobilized laccase showed consistent performance across four decolorization cycles. Moreover, the breakdown products of the azo dye mix were analyzed using the HPLC method, complemented by evaluations of potential antimicrobial activity, phytotoxicity and cytotoxicity, revealing a non-toxic composition without cytotoxicity, highlighting the process's safety for environmental release. The significance of this research is reflected in the distinguished construction of a green biocatalyst acting as a stable and time-efficient in remediation of targeted azo dye pollution from the textile industry following the principles of circular economy.

Controllable preparation and performance study of DAP-4/CuO high-energy microspheres

To enhance the dispersion uniformity of DAP-4/CuO composite fuel and improve the stability and consistency of the entire charge, particularly in small doses, DAP-4/CuO composite microspheres were prepared using a self-built coaxial microfluidic platform. This addressed the issue of uneven dispersion of DAP-4 and CuO, resulting in excellent dispersion of DAP-4/CuO composite microspheres. Furthermore, the impact of CuO content on DAP-4 microspheres was investigated. The samples were analyzed using scanning electron microscopy, laser confocal microscopy, differential scanning calorimetry, high-speed photography, and infrared thermal imaging. The findings revealed that when 1 % NC was added to the binder, FN-5 components were evenly dispersed. Microspheres produced through microfluidic technology exhibited a narrow particle size distribution which significantly improved sample dispersion and enhanced charging process safety. The addition of CuO notably accelerated the thermal decomposition process of DAP-4 microspheres and substantially increased sample burning rate. However, when CuO content exceeded 10 %, further increases did not continue to raise burning rate but led to decreased flame temperature. This study provides a reference for relevant workers.

A hierarchical multi-parametric programming approach for dynamic risk-based model predictive quality control

In this work, we present a hierarchical batch quality control strategy with real-time process safety management. It features a multi-time-scale decision-making framework augmenting: (i) Risk-aware model predictive controller for short-term set point tracking and dynamic risk control under disturbances; (ii) Control-aware optimizer for long-term quality and safety optimization over the entire batch operation; (iii) Intermediate surrogate model to bridge the timescale gap by readjusting the optimizer operating decisions for the controller. All of the above problems are solved via multi-parametric mixed-integer quadratic programming with a key advantage to generate offline explicit control/optimization laws as affine functions of process and risk variables. This allows for the design of a fit-for-purpose risk management plan prior to real-time implementation, while reducing the need for repetitive online dynamic optimization. A unified process model is used to underpin the consistency of hierarchical operational optimization. The proposed approach offers a flexible strategy to integrate distinct decision-making time scales which can be selected separately tailored to the process-specific need of control, fault prognosis, and end-batch quality control. A T2 batch reactor case study is presented to showcase this approach to systematically address the interactions and trade-offs of multiple decision layers toward improving process efficiency and safety.

Simultaneous utilization of copper and non-metals from waste integrated circuits for precious metal melting capture and organics thermal detoxification

Waste integrated circuits, with underlying resource attributes and environmental risks, are complex in composition. Current processes suffer from high resource inputs, excessive pollutant emissions and incomplete treatment of varied species. Therefore, a comprehensive resource recycling and safe disposal process integrated with precious metal enrichment, non-metal resource utilization, and organic detoxification was proposed. The copper and non-metals from waste ICs served as in-situ precious metal collector, slagging former and reducing agent for alternative to partial chemical inputs. Batch experiments indicated that optimized parameters enabled the recycling efficiencies of 94.3 %, 96.8 % and 98.4 % for copper, gold and silver, respectively. Meanwhile, oxide component, yielded as silicate slag, was synthesized into glass ceramics via high-temperature sintering. Furthermore, organic conversion process revealed that high-temperature smelting catalyzed bromine removal and contaminant detoxification, with decomposed reductive hydrocarbons facilitating metal capture in turn. And the capture mechanism was disclosed form thermodynamics and microdroplet motion perspectives. As process evaluation indicates, proposed recycling route allows remarkable reductions in negative environmental response and economic investment. In this work, metal recycling, waste minimization and pollutant detoxification were attained synergistically without residue, which contributes a novel insight into the disposal of comparable e-wastes.

Diagnosing electrostatic problems and hazards in industrial processes: Case studies

AbstractStatic electricity is a phenomenon commonly encountered yet often misunderstood and underestimated in terms of its hazard potential; it continues to challenge the safety and reliability of industrial processes, particularly those involving flammable substances. This paper delves into the critical issue of electrostatic hazards in industrial settings by presenting two flash fire/explosion case studies, one involving biphenyl dust and the other gasoline vapor; both linked to electrostatic discharges. The first case study examines an explosion in a flaker and pack‐out hopper during biphenyl flake manufacturing. The investigation reveals the role of electrostatic charges in the incident and how well‐intentioned equipment changes created warning signs of increased risk that were missed. The second case study discusses a gasoline vapor flash fire, highlighting the common yet overlooked hazard of static electricity during the transfer of flammable liquids. It underscores how common activities involving people can generate sufficient electrostatic charge to ignite flammable vapor–air mixtures in industry. The outcomes of these studies highlight the importance of acting on early warning signs of static electricity and the crucial role of data‐driven diagnostic techniques in addressing and controlling those hazards. By dissecting the intricacies of electrostatic phenomena, safety professionals can formulate actionable strategies to adapt plant operations and prevent hazards from static electricity. The paper advocates for a proactive approach to identifying and mitigating electrostatic risks in industrial settings beginning with process hazard analyses that incorporate static electricity hazards analysis and continues through employee training to ensure that early warning signs are identified, understood, and acted upon. It stresses the need for comprehensive safety measures, including proper grounding and bonding, use of static dissipative materials, and regular maintenance of safety equipment, to prevent incidents. Through these case studies, the paper contributes to understanding of electrostatic hazards in industrial processes and highlights the importance of integrating electrostatic safety measures into routine industrial operations.

H∞ Differential Game of Nonlinear Half-Car Active Suspension via Off-Policy Reinforcement Learning

This paper investigates a parameter-free H∞ differential game approach for nonlinear active vehicle suspensions. The study accounts for the geometric nonlinearity of the half-car active suspension and the cubic nonlinearity of the damping elements. The nonlinear H∞ control problem is reformulated as a zero-sum game between two players, leading to the establishment of the Hamilton–Jacobi–Isaacs (HJI) equation with a Nash equilibrium solution. To minimize reliance on model parameters during the solution process, an actor–critic framework employing neural networks is utilized to approximate the control policy and value function. An off-policy reinforcement learning method is implemented to iteratively solve the HJI equation. In this approach, the disturbance policy is derived directly from the value function, requiring only a limited amount of driving data to approximate the HJI equation’s solution. The primary innovation of this method lies in its capacity to effectively address system nonlinearities without the need for model parameters, making it particularly advantageous for practical engineering applications. Numerical simulations confirm the method’s effectiveness and applicable range. The off-policy reinforcement learning approach ensures the safety of the design process. For low-frequency road disturbances, the designed H∞ control policy enhances both ride comfort and stability.

Risk and consequence analysis of ammonia storage units in a nuclear fuel cycle facility

AbstractIndustrial disasters, such as unintended toxic gas releases resulting in fires, explosion, and fatalities, are damaging both the global ecology and the social acceptance of the related technology. Risk and consequence analysis is the key feature of process safety measures for the protection of environment and human life. In this work, the risk and consequence analysis of unintended release of anhydrous liquid ammonia for one of the storage tanks, located inside the nuclear fuel cycle facility, was analyzed for leakages leading to exposure of surrounding human population and fire with allied thermal radiation risk. The risk assessment was performed using four methods: Fault tree, E&FI methodology, Probit, and ALOHA, It was observed that while the predicted hazard severities from fault‐tree analysis, Probit analysis, and ALOHA nearly converged, the conventional F&EI, the safety workhorse of the chemical industry estimated low numeric values of the respective hazard. A methodology was proposed to load this general F&EI value for the case of chemical plant being located inside a nuclear facility. Overloaded F&EI procedure estimated hazard value for envisaged unintended ammonia release in good agreement, with results from FTA, Probit, and ALOHA analyses.

A Review of CIGS Thin Film Semiconductor Deposition via Sputtering and Thermal Evaporation for Solar Cell Applications

Over the last two decades, thin film solar cell technology has made notable progress, presenting a competitive alternative to silicon-based solar counterparts. CIGS (CuIn1−xGaxSe2) solar cells, leveraging the tunable optoelectronic properties of the CIGS absorber layer, currently stand out with the highest power conversion efficiency among second-generation solar cells. Various deposition techniques, such as co-evaporation using Cu, In, Ga, and Se elemental sources, the sequential selenization/sulfidation of sputtered metallic precursors (Cu, In, and Ga), or non-vacuum methods involving the application of specialized inks onto a substrate followed by annealing, can be employed to form CIGS films as light absorbers. While co-evaporation demonstrates exceptional qualities in CIGS thin film production, challenges persist in controlling composition and scaling up the technology. On the other hand, magnetron sputtering techniques show promise in addressing these issues, with ongoing research emphasizing the adoption of simplified and safe manufacturing processes while maintaining high-quality CIGS film production. This review delves into the evolution of CIGS thin films for solar applications, specifically examining their development through physical vapor deposition methods including thermal evaporation and magnetron sputtering. The first section elucidates the structure and characteristics of CIGS-based solar cells, followed by an exploration of the challenges associated with employing solution-based deposition techniques for CIGS fabrication. The second part of this review focuses on the intricacies of controlling the properties of CIGS-absorbing materials deposited via various processes and the subsequent impact on energy conversion performance. This analysis extends to a detailed examination of the deposition processes involved in co-evaporation and magnetron sputtering, encompassing one-stage, two-stage, three-stage, one-step, and two-step methodologies. At the end, this review discusses the prospective next-generation strategies aimed at improving the performance of CIGS-based solar cells. This paper provides an overview of the present research state of CIGS solar cells, with an emphasis on deposition techniques, allowing for a better understanding of the relationship between CIGS thin film properties and solar cell efficiency. Thus, a roadmap for selecting the most appropriate deposition technique is created. By analyzing existing research, this review can assist researchers in this field in identifying gaps, which can then be used as inspiration for future research.

Resilience-based explainable reinforcement learning in chemical process safety

For future applications of artificial intelligence, namely reinforcement learning (RL), we develop a resilience-based explainable RL agent to make decisions about the activation of mitigation systems. The applied reinforcement learning algorithm is Deep Q-learning and the reward function is resilience. We investigate two explainable reinforcement learning methods, which are the decision tree, as a policy-explaining method, and the Shapley value as a state-explaining method.The policy can be visualized in the agent’s state space using a decision tree for better understanding. We compare the agent’s decision boundary with the runaway boundaries defined by runaway criteria, namely the divergence criterion and modified dynamic condition. Shapley value explains the contribution of the state variables on the behavior of the agent over time. The results show that the decisions of the artificial agent in a resilience-based mitigation system can be explained and can be presented in a transparent way.

A Lyapunov-based adaptive control strategy with fault-tolerant objectives for proton exchange membrane fuel cell air supply systems

Proton exchange membrane fuel cells (PEMFCs) are being extensively studied for large-scale applications. The need for reliable and safe system integration processes emphasizes the importance of health-conscious control research. Considering their high-frequency operation and load variations, the reliability of PEMFCs can be compromised by mechanical failures within air supply systems. Therefore, this paper proposes an adaptive control strategy with fault-tolerant objectives for regulating the oxygen excess ratio (OER). Specifically, it addresses two common faults within air compressors, namely compressor overheating and increased mechanical friction. While these faults have been studied in fault diagnosis research, their treatment within the context of fault-tolerant control is relatively uncommon. To ensure reliable OER regulation under multiple fault occurrences, estimator-based parameter adaptation laws are firstly derived using Lyapunov theory during the stability demonstration process. Then, the baseline controller is designed using an extended state observer and feedback linearization, with a clear presentation of the existence and stability of the state transformation. Furthermore, extensive numerical tests are conducted to demonstrate the effectiveness of the proposed control strategy. The proposed adaptive control strategy ensures consistent regulation outcomes without compromising performance under healthy conditions, highlighting its potential for large-scale application as a standard control approach in practical applications.

Artificial intelligence (AI) and process safety: Some cautionary observations

AbstractArtificial intelligence (AI) has become a vogue topic in the press, and descriptions of its potential impact range from apocalyptic to salvational. Interest in the topic will no doubt stimulate the search for applications to support both the technical and management systems aspects of process safety management. Within our industries, maintaining institutional memory and technical capability is made increasingly challenging by more frequent job movement among younger staff and the loss to the retirement of more senior staff. One would hope that AI could help fill the gaps caused by these factors. However, the author's sampling of current AI capabilities suggests that AI is not yet ready to do so. This paper provides some examples of errors and insufficiencies identified when seeking AI assistance in addressing process safety issues. It also suggests some existing challenges to better “training” of AI to support the needs of the process safety community. It concludes that caution should be applied, especially by less experienced personnel, when seeking AI assistance in addressing process safety–related technical matters.

Ag-Cu2O Supported Biomass-Derived rGO for Catalyzing Suzuki-Miyaura Cross-Coupling.

The search for cost-effective, efficient, and ecofriendly heterogeneous catalysts for the Suzuki-Miyaura reaction is crucial due to challenges with expensive, toxic homogeneous catalysts. This study centrally aims at crafting a pioneering green catalyst by adorning reduced graphene oxide (rGO), sourced from basil seeds (Ocimum basilicum L.), with an Ag-Cu2O composite structure. Comprehensive characterization of the Ag-Cu2O/rGO nanocomposite was conducted through FTIR, SEM, hHR-TEM, EDS, XPS, XRD, TGA, and N2 adsorption/desorption analyses. Results showed that nanosized Ag-Cu2O particles were partially integrated into rGO sheets derived from basil seeds, acting as active species for oxidative addition with aryl halides in the SMR. The catalytic efficacy of this robust nanocatalyst was assessed in Suzuki-Miyaura cross-coupling reactions, targeting the synthesis of biaryls employing various aryl halides and aryl boronic acids. The findings underscore that the Ag-Cu2O/rGO nanocatalyst manifests rapid reaction kinetics (15 min) alongside commendable yields (99%). The Ag-Cu2O/rGO demonstrates impressive recyclability, maintaining catalytic efficiency over four cycles. Utilizing it as a green substrate for metal loading highlights its potential, offering well-defined coordination sites. This approach facilitates stable heterogeneous catalyst fabrication, crucial for significant bond formations. Notable features include broad applicability, exceptional functional tolerance, scalability, and practicality. Moreover, it holds promise for automating safe processes and enabling efficient late-stage functionalization of complex molecules with moderate to high efficiency, presenting promising prospects for various applications in chemical synthesis.

Design and Implementation of Remote Lab PID Controller Experiment Based on IoT

The education sector has increasingly embraced distance education to ensure a safe and uninterrupted learning process, especially in wars or epidemics. This paper focuses on designing and implementing a PID (Proportional-Integral-Derivative) controller experiment in remote laboratories utilizing the Internet of Things (IoT). Also, a laboratory experiment was developed using a naturally unstable system, specifically a cart-inverted pendulum. The experiments aim to enhance students' understanding of PID controller tuning within a real-world context. This system was chosen due to its dual motion characteristics, with a linear motion for the cart and a circular motion for the pendulum. Two controllers were designed and implemented for the system to enable control and feedback. Additionally, the Blynk platform was integrated into the experiment setup, allowing for real-time visualization of the system's response, control of PID parameters, and the ability to view the video stream via platforms like Skype. For remote connectivity, NodeMCU, an IoT development board, controlled the pendulum, collected system parameters, and transmitted them to the cloud through the Internet. Moreover, the sensors and the system were mathematically modeled, and their transfer functions were extracted. That allows the students to do the PID experiment theoretically and practically and compare the results. This complete setup enables local and remote access to the experiment, ensuring students can experiment regardless of their physical location. As a result of the study, designing and implementing a remote laboratory PID controller experiment utilizing IoT technology provides students with an innovative and immersive learning experience.

On-Line Detection Method of Salted Egg Yolks with Impurities Based on Improved YOLOv7 Combined with DeepSORT.

Salted duck egg yolk, a key ingredient in various specialty foods in China, frequently contains broken eggshell fragments embedded in the yolk due to high-speed shell-breaking processes, which pose significant food safety risks. This paper presents an online detection method, YOLOv7-SEY-DeepSORT (salted egg yolk, SEY), designed to integrate an enhanced YOLOv7 with DeepSORT for real-time and accurate identification of salted egg yolks with impurities on production lines. The proposed method utilizes YOLOv7 as the core network, incorporating multiple Coordinate Attention (CA) modules in its Neck section to enhance the extraction of subtle eggshell impurities. To address the impact of imbalanced sample proportions on detection accuracy, the Focal-EIoU loss function is employed, adaptively adjusting bounding box loss values to ensure precise localization of yolks with impurities in images. The backbone network is replaced with the lightweight MobileOne neural network to reduce model parameters and improve real-time detection performance. DeepSORT is used for matching and tracking yolk targets across frames, accommodating rotational variations. Experimental results demonstrate that YOLOv7-SEY-DeepSORT achieves a mean average precision (mAP) of 0.931, reflecting a 0.53% improvement over the original YOLOv7. The method also shows enhanced tracking performance, with Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) scores of 87.9% and 73.8%, respectively, representing increases of 17.0% and 9.8% over SORT and 2.9% and 4.7% over Tracktor. Overall, the proposed method balances high detection accuracy with real-time performance, surpassing other mainstream object detection methods in comprehensive performance. Thus, it provides a robust solution for the rapid and accurate detection of defective salted egg yolks and offers a technical foundation and reference for future research on the automated and safe processing of egg products.

Distributed monitoring of nonlinear plant-wide processes based on GA-regularized kernel canonical correlation analysis

Fault detection and diagnosis is important for ensuring process safety and is gaining increasing attention in the system safety field. A regularized kernel canonical correlation analysis (RKCCA) approach is proposed for monitoring nonlinear plantwide processes. For each local unit, genetic algorithm (GA)-based regularization is performed to determine the communication variables from neighboring units, which preserves the maximum correlations and eliminates the irrelevant variables. Then variables from a local unit and the communication variables are mapped into high-dimensional feature spaces, and the feature space of the local unit is decomposed into three orthogonal subspaces, namely the residual subspace, the inner subspace, and the outer-related subspace. Monitoring statistics to identify both the process status and the characteristic of a detected fault are constructed. The proposed RKCCA-based monitoring method considers both the information of a local unit and the beneficial information of related units to facilitate fault detection, thereby exhibiting superior performance to some state-of-the-arts methods. Applications on the Tennessee Eastman benchmark process and an industrial tail gas treatment process demonstrate the superiority of RKCCA monitoring.

Integrating process safety management into Canadian wood pellet facilities that generate combustible wood dust

Integrating process safety management into Canadian wood pellet facilities that generate combustible wood dust

A novel fault detection and identification method for complex chemical processes based on OSCAE and CNN

The safety and reliability of chemical processes are of paramount importance. However, due to the complicated heat, mass, and momentum transfer processes coupled with chemical reactions, and the interrelated effects among various equipment units, it is difficult to accurately detect and identify faults occurring in the chemical system. This study proposes an innovative fault diagnosis framework specifically designed for chemical systems. A method based on orthogonal self-attention convolutional autoencoder (OSCAE) is constructed for fault detection. The convolutional autoencoder is combined with an orthogonal attention mechanism to extract time-series characteristic information of the operational state, thereby achieving precise detection of fault conditions. Additionally, a convolutional neural network (CNN) model uses both raw signals and data reconstructed by OSCAE to identify different faults, which enhances the features of the input and enables highly accurate fault identification. The effectiveness and robustness of the propose method is validated with simulation data of our self-established chemical distillation system and the publicly available Tennessee Eastman process system. The diagnosis efficacy in fault detection and identification is validated by applying fault detection metrics, high-dimensional feature visualizations, and confusion matrix analyses across two case studies. This paper not only provides an effective diagnostic framework for chemical systems but also offers a usable benchmark dataset for chemical distillation systems to the academic community, with the hope that the research content can enhance the stability and reliability of chemical processes.

Process Safety Maturity Assessment Tool

AbstractDue to rapid change from divestitures, acquisitions, and greenfield startups, Albemarle found it challenging to communicate and assess our Process Safety expectations. We decided we needed a tool that would both guide site management through the milestones related to process safety management systems and one that would allow senior management to assess progress. In addition, a tool was needed to challenge existing sites with continuous improvement goals in process safety management. For this reason, one key aspect of the tool is applicability to every site in the company; even though the maturity of the process safety program is diverse.

Addressing safety and sustainability issues in the development of nano-enabled MULTI-FUNctional materials for metal additive manufacturing

This paper presents a comprehensive investigation into the sustainable development of nano-enabled multifunctional materials (NMMs) for metal additive manufacturing (MAM), based on the principles of the Safe and Sustainable-by-Design (SSbD) framework. The work focuses on both environmental impacts and health risks potentially associated with these materials, offering a groundwork for a more complete SSbD assessment that includes socio-economic considerations.The Life Cycle Assessment (LCA) identified significant environmental advantages of using Titanium Nitride (TiN) and Chromium Nitride (CrN) in wire coating processes over Titanium Carbide (TiC), with a 77% reduction in environmental impacts. However, despite the emphasis on environmental aspects during materials selection, the primary environmental hotspot is the heat treatment stage used during the process to shape NMMs into pellets. Eco-toxicity assessments performed on both nano- and microparticles from the studied NMMs for MAM, identified specific thresholds to ensure over 80% cell viability, labelling MoCu and CCuCr as particularly cytotoxic. Additionally, a task-based risk assessment in laboratory environments, evaluating the potential release of engineered nanoparticles (ENPs), identified minimal inhalation exposure risk for workers due to effective control measures implemented.Overall, by integrating findings on eco-toxicity, worker safety, and environmental hotspots in production processes, this study offers an approach for advancing sustainable practices during the production of NMMs for MAM in laboratory settings. These findings can be valuable for future research and development in the field, ensuring that sustainability and safety are integral to the advancement of nanotechnology.