Large-scale Industrial Plants Research Articles

Time-Sensitive Networking (TSN) for Industrial Automation: Current Advances and Future Directions

With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) technologies, the automation industry is undergoing significant changes, particularly in improving production efficiency and reducing maintenance costs. Industrial automation applications often need to transmit time- and safety-critical data to closely monitor and control industrial processes. Several Ethernet-based fieldbus solutions, such as PROFINET IRT, EtherNet/IP, and EtherCAT, are widely used to ensure real-time communications in industrial automation systems. These solutions, however, commonly incorporate additional mechanisms to provide latency guarantees, making their interoperability a grand challenge. The IEEE 802.1 Time-Sensitive Networking (TSN) task group was formed to enhance and optimize IEEE 802.1 network standards, particularly for Ethernet-based networks. These solutions can be evolved and adapted for cross-industry scenarios, such as large-scale distributed industrial plants requiring multiple industrial entities to work collaboratively. This paper provides a comprehensive review of current advances in TSN standards for industrial automation. It presents the state-of-the-art IEEE TSN standards and discusses the opportunities and challenges of integrating TSN into the automation industry. Some promising research directions are also highlighted for applying TSN technologies to industrial automation applications.

Biofuel production utilizing black soldier fly (Hermetia illucens): a sustainable approach for organic waste management

This study investigates the potential of biodiesel production from Black Soldier Fly (BSF) larvae using organic waste. The aim of this research is to assess the feasibility of biodiesel production and its economic viability from BSF larvae. Specific objectives include analyzing organic waste quantity, moisture content, dry matter, BOD content, and biodiesel conversion efficiency. Our findings reveal that under various scales, biodiesel production from BSF larvae shows promise. For instance, in a small-scale urban biofuel plant, we estimate the production of approximately 122.73 liters of biodiesel per day from 1 ton of organic waste. Moreover, our calculations demonstrate the potential for medium-scale rural and large-scale industrial biofuel plants to produce 1,678.85 and 22,272.73 liters of biodiesel per day, respectively. These results highlight the significant biodiesel potential of BSF larvae. In conclusion, this study provides valuable insights into the biodiesel production potential of BSF larvae. It underscores the feasibility of utilizing organic waste for biodiesel production, contributing to sustainable biofuel development. Our research contributes to the knowledge base by offering quantitative assessments of biodiesel potential. We have incorporated quantitative data to provide a clearer understanding of the biodiesel potential and its economic implications.

Towards Sustainable Hydrogen Peroxide Electroproduction: Activated Carbon from Sewage Sludge As an Eco-Friendly Electrode Material

Hydrogen peroxide (H2O2) has emerged as a chemical of paramount importance, finding diverse applications as a bleaching agent, medical disinfectant, and environmentally benign oxidant [1]. The industrial production of H2O2 currently relies on the anthraquinone oxidation process, primarily executed within large-scale industrial plants [1]. Herein lies a challenge— H2O2, typically concentrated to an 80% level, introduces inherent hazards concerning storage and transportation, prompting a quest for a safer alternative. One way to overcome such problems is by the in-situ electrogeneration of H2O2, which would allow an adjustable production of the chemical on demand directly for its application, avoiding storing costs and safety issues. The in-situ electrogeneration can be done by using a gaseous-diffusion electrode (GDE), which typically enables the application of elevated current densities or substantial overpotentials, consequently facilitating the generation of high concentrations of H2O2, since there is less limitation due to O2 mass transport to the electrode surface [2]. Commonly, these electrodes are constructed from amorphous carbon materials, due to their highly active surface area with oxygenated functional groups, excellent conductivity, and innate selectivity for H2O2 electroproduction. However, a notable challenge emerges – bare carbon's selectivity for H2O2 presents its highest response at high overpotentials, entailing heightened energy consumption. The studies in this field are currently focused on using these types of carbon as support for other catalytic materials that are more active and selective towards H2O2 electroproduction [3, 4]. Numerous studies in the literature have explored the use of carbon materials, with the most commonly employed carbon supports for such applications being Printex 6L (Orion) and Vulcan XC 72R (Cabot). These materials are obtained from the incomplete combustion of heavy petroleum products at high temperatures, being non-sustainable and environmentally unfriendly. A potential solution lies in the use of environmentally conscious carbon sources. While amorphous carbon can be obtained from various carbon-rich wastes through an activation process, their utility in H2O2 electrogeneration remains underexplored. This study sought to obtain activated carbon from sewage sludge, focusing on its efficacy in H2O2 electrogeneration. Acid (H3PO4) and alkaline (KOH/NaOH mixture) carbon activators were analyzed in concentrations ranging from 0 – 30 %m/v, temperatures of 400 – 900 ºC, and activation time from 0 – 2 h at the selected temperature. The obtained materials were analyzed through linear sweep voltammetry using an RRDE setup in N2-saturated and O2-saturated, in a 0.05 M K2SO4 (pH 3) electrolyte solution. Results have shown that the most successful materials for H2O2 electrogeneration were those produced under 900 ºC for 2h impregnated with 5 % m/v H3PO4 and 597 ºC, zero of plateau time impregnated with 20 % m/V mixture of KOH/NaOH in the ratio of 50.5 KOH: NaOH. These optimized materials exhibited remarkable selectivities of ≈ 90% and ≈ 81% at -0.8 V vs. Ag/AgCl, respectively. This level of electrocatalytic performance is on par with that of the commercial reference material, Printex 6L. These findings highlight the promise of our material as a viable carbon support for in-situ H2O2 electrogeneration, offering a more sustainable alternative compared to commercial black carbons. Acknowledgments The authors are sincerely grateful to the Brazilian research funding agencies, including the Brazilian National Council for Scientific and Technological Development - CNPq (grants no. 465571/2014-0, 302874/2017-8, and 427452/2018-0), São Paulo Research Foundation (FAPESP – grants no. #2023/04230-2, #2023/07750-7, 2022/04058-2, #2021/12053-8, #2019/04421-7, #2017/10118-0, and #2013/02762-5).

Pilot Scale Production of Precast Concrete Elements with Wood Biomass Ash.

Downsizing fossil fuel dependence and greenhouse gas emissions is at the forefront of a sustainable future. The expansion of renewable energy while striving to minimize dependence on fossil fuels has led to biomass taking the lead among renewable energy sources, with wood having the broadest application. Along with the growing trend of using biomass as a renewable energy source, the combustion of wood biomass results in wood biomass ash (WBA), leading to compelling amounts of waste. In this study, the technical feasibility of fly WBA from different Croatian power plants was analyzed to evaluate its potential use in precast concrete drainage elements and curb units. By implementing a performance-based design, the influence of various factors in thermal processing of wood biomass was investigated, together with a detailed characterization of WBA in order to assess the feasibility of using WBA as a secondary raw material in a large-scale industrial batching plant. The compressive strength and durability properties (water absorption, permeability, and freeze–thaw resistance) of concrete mixtures with WBA as a replacement for 15 wt% cement were evaluated and compared with the precast concrete manufacturer’s technical requirements. The main concerns identified were compositional inconsistency of WBA, workability downturn, delay in initial reactivity rate, and increased water absorption. Concrete with WBA based on a circular design has been found to be a viable solution to cement depletion, stepping up from recycling to reuse of industrial waste.

Energy-based fault detection and isolation of a Brayton cycle-based HTGR power conversion unit – A comparative study

The amount of data associated with condition monitoring of large scale industrial plants has led to a “big data problem”. Energy-based fault detection and isolation (FDI) has been proposed as a hybrid FDI method to reduce data requirements and improve FDI performance. In this paper, a comparative study is performed on three energy-based FDI techniques, namely: an enthalpy–entropy error-based technique, a residual-based technique and an eigendecomposition-based technique. A representative Brayton cycle-based power conversion unit (PCU) is considered as the case study and simulated under normal and fault conditions. From the simulation data, the PCU is represented in terms of energy, either as an enthalpy–entropy diagram or an attributed graph. The graph representation allows structural information to be captured which contributes towards fault isolation capability. For each FDI technique, operational energy signatures are compared to normal and fault reference energy signatures to detect and isolate fault conditions. This paper contributes by comparing the FDI techniques based on the attributes of quick detection and isolation. The residual-based technique had the best performance in detecting and isolating faults. The results also indicate that a combination of techniques will result in an FDI technique with improved detection and isolation performance over the individual techniques.

The environmental performance of enhanced metal recovery from dry municipal solid waste incineration bottom ash

This study assesses the environmental performance of the municipal solid waste (MSW) incineration bottom ash (IBA) treatment plant in Hinwil, Switzerland, a large-scale industrial plant, which also serves as a full-scale laboratory for new technologies and aims at an optimal recovery of metals in terms of quantity and quality. Based on new mass-flow data, we perform a life cycle assessment that includes the recovery of iron, stainless steel, aluminium, copper, lead, silver and gold. Fraction-specific modelling allows for investigating the effect of the metal fraction quality on the subsequent secondary metal production as well as examining further metal recycling potentials in the residual IBA. In addition, the implications on the landfill emissions of IBA residues to water were quantified. The impact assessment considered climate change, eco- and human toxicity and abiotic resource depletion as indicators.Results indicate large environmental savings for every impact category, due to primary metal substitution and reduction of long-term emissions from landfills. Metal product substitution contributes between 75% and >99% to these savings in a base scenario (1′000-year time horizon), depending on the impact category. Reductions in landfill emissions become important only when a much longer time horizon was adopted. The metal-based analysis further illustrates that recovering heavy non-ferrous metals – especially copper and gold – leads to large environmental benefits. Compared to the total net savings of energy recovery (215 kg CO2-eq per tonne of treated waste, average Swiss plant), enhanced metal recovery may save up to 140 kg CO2-eq per tonne of treated waste.

Hierarchical Quality Monitoring for Large-Scale Industrial Plants With Big Process Data.

For large-scale industrial plants, quality-related process monitoring is challenging because of the complex features of multiunit, multimode, high-dimension data. Hence, a hierarchical quality monitoring (HQM) algorithm based on the distributed parallel semisupervised Gaussian mixture model (dp-S2GMM) is proposed in this article. In HQM, a large-scale process is first decomposed into a group of unit blocks according to the process structure. Subsequently, in each block, a quality regression model with multimode big process data is built using the dp-S2GMM, which is derived from a scalable stochastic variational inference semisupervised GMM (SVI-S2GMM). With the regression model, a hierarchical fault detection and diagnosis scheme in both quality-related and quality-unrelated subspaces is proposed from the variable level, block level to plant-wide level. Finally, an industrial case study on the Tennessee Eastman process demonstrates the feasibility and effectiveness of the proposed HQM algorithm.

Optimal operation model of integrated energy system for industrial plants considering cascade utilisation of heat energy

As the energy crisis becomes more prominent, the issue of energy conservation and recycling is urgent. For large-scale industrial plants, there are various forms of heat energy resources with different grades and energy storages. This study presents the detail modelling of all equipment in the integrated energy system to specify the physical operational constraints. Furthermore, an optimisation model is set up to minimise the total cost of an individual industrial plant, with the consideration of energy production equipment, energy conversion equipment and energy storage equipment in the plant. In particular, the relationship among the heat energy with different grades is described to characterise the cascade utilisation of the heat energy. Finally, a material processing factory with multi-grade heat energy is simulated to verify the effectiveness of the proposed model with energy cascade utilisation.

Dimensionality reduction for visualizing industrial chemical process data

This paper explores dimensionality reduction (DR) approaches for visualizing high dimensional data in chemical processes. Visualization provides powerful insight and process understanding in the industrial context, and accelerates process troubleshooting. A diverse array of existing, easy-to-use DR methods are evaluated in three case studies on large-scale industrial manufacturing plants. Supervised and unsupervised cases are presented with the objective of solving typical industrial problems related to unplanned events, plant performance improvement, and quality underperformance troubleshooting. For the unsupervised case, the evaluation aims to identify approaches that provide insight beyond those of PCA (Principal Component Analysis), and also examines quality metrics of the reduced (latent) space which characterize the degree of trust in the DR. UMAP (Uniform Manifold Approximation and Projection) outperforms other techniques, bringing new insights when comparing with other methods. For the supervised case, UMAP is combined with traditional variable selection methods, such as VIP (Variable Influence on Projection) weights from PLS-DA (Partial Least Squares Discriminant Analysis), in order to improve latent space visualization by increasing separation between classes.

Multi-product continuous plant scheduling: combination of decomposition, genetic algorithm, and constructive heuristic

We propose a polylithic method for medium-term scheduling of a large-scale industrial plant operating in a continuous mode. The method combines a decomposition approach, a genetic algorithm (GA) and a constructive MILP-based heuristic. In the decomposition, decisions are made at two levels, using the rolling horizon approach. At the upper level, a reduced set of products and the time period is chosen to be considered in the lower level. At the lower level, a short-term scheduling MILP-model with event-based representation is used. A heuristic solution to the lower level problem is found using a constructive Moving Window heuristic guided by a genetic algorithm. The GA is applied for finding efficient utilisation of critical units in the lower level problem. For solving the one unit scheduling problem, a parallel dynamic programming algorithm is proposed. Implementation of the dynamic programming algorithm for a graphics processing unit (GPU) is incorporated in the GA for improving its performance. The experimental study of the proposed method on a real case of a large-scale plant shows a significant improvement of the solution quality and the solving time comparing to the pure decomposition algorithm proposed in the earlier study, and confirmed suitability of the proposed approach for the real-life production scheduling. In particular, the reduction of the number of changeovers and their duration in the obtained solution as well as the CPU time of solving the problem was about 60% using the new approach.

Group maintenance scheduling for two-component systems with failure interaction

• A novel failure interaction framework is constructed considering hazard rate increment. • Environmental uncertainties are incorporated into dependent failure processes. • A new group maintenance strategy is developed to prevent dependent failures. • The applicability is validated by a case study on an electrical distribution system. Failure dependence is a common phenomenon in complex and large-scale industrial plants, which may cause tremendous economic losses and unacceptable safety risks. This paper investigates an advanced group and opportunistic maintenance policy for a two-component system with failure interaction. The interaction between soft and hard failures is characterized by random hazard rate increments, with fully consideration of environmental uncertainties. To sufficiently utilize the economic dependence between components, Scheduled maintenance windows are equally spaced for group maintenance of the whole system. Whenever a component is replaced at a maintenance window, the other one is also opportunistically replaced. Furthermore, failure of component 2 is removed by minimal repair, whereas replacement is left to the subsequent window. The objective is to jointly optimize the maintenance interval and the number of maintenance windows via the artificial bee colony algorithm. A case study on an electrical distribution system is provided to validate the applicability of the adopted policy.

Modeling and Optimization of a Large-Scale Ethylene Plant Energy System with Energy Structure Analysis and Management

The energy system of industrial process, particularly in the petrochemical industry, consumes most of the utility cost. In this paper, a superstructure of a large-scale industrial ethylene plant en...

An industrial facility layout design method considering energy saving based on surplus rectangle fill algorithm

The facility layout design within a plant is a key step in the process of chemical engineering design. A good layout can save capital cost, energy and natural resource. The aim of this work is to determine the relative location of facilities in a large-scale industrial plant considering multi-floor structure to make the total cost minimum. The objective function consists of piping investment cost, pump power cost, land cost, and floor construction cost. Surplus rectangle fill algorithm is applied in this work, and it is combined with a genetic algorithm to obtain the optimal solution. Constraints of pump area, joint arrangement of heat exchangers, and the cross-floor facilities are also taken into consideration. In the case study, a plant from a real refinery including 217 facilities is designed with different floor number. The comparison of the three different conditions proves the important role of floor number in the trade-off among investment cost, energy consumption, and land resource. The case illustrates that, the proposed method can generate a reasonable layout design for the industrial facilities and save capital and operating cost effectively. A sensitivity analysis is also done to explore the influence of basic data on the optimal construction structure.

Operator Training Simulator for an Industrial Bioethanol Plant

Operator training simulators (OTS) are software tools for training process operators in large-scale industrial applications. Here, we describe the development, implementation and training of an OTS for a large-scale industrial plant for bioethanol production. The design of the OTS is based on conceptual analysis (previously reported by us in this journal) of various configuration alternatives and training procedures at the plant. In this article, we report on how the conceptual design is used in simulation models and graphical user interfaces and how the design is applied for training of operators in the real plant environment. The results imply that OTS would be time- and cost-efficient tools for application in the biotechnological industry.

A generic framework for distributed multi-generation and multi-storage energy systems

We have introduced a generic decision support tool for concurrent optimal selection, sizing, and operation scheduling of grid-connected or off-grid multi-generation/multi-storage distributed generation and storage (DGS) systems with respect to the dynamics of historical/projected periodical weather data, electricity price, DGS system cost, DGS aging, and the major critical design and operational parameters. This decision support program enables the consumer (ranging from a small house to large-scale industrial plants) to implement the most efficient electricity management strategy while achieving the goal of minimizing the electricity bill.

Direct production of high octane gasoline and ULSD blend stocks by LCO hydrocracking

Hydrocracking of light cycle oil (LCO) is of great scientific and industrial importance to obtain high quality of gasoline and diesel. In this work, a novel LCO hydrocracking technology (FD2G) was proposed, and three kinds of hydroprocessing catalysts (i.e., FC-14, FC-24 and FC-26) were tested under different pilot-scale operational conditions and/or types. All the three catalysts are found to be active for direct production of high octane gasoline and ultra low sulfur diesel (ULSD) blend stocks, and the FC-24 catalyst is the most active and selective toward production of high octane gasoline. The quality of gasoline and diesel products is highly sensitive to pressure and operation type. Furthermore, the FC-24 catalyst was also tested in a large-scale industrial plant. It is found that the yield of gasoline blend stock reaches 30–50wt%, the octane number of gasoline is 91–94 and the sulfur content is less than 10μg/g; the cetane number of diesel blend stock increases 10–14 units in comparison to the feed stock, and the sulfur content is less than 10μg/g.

Sensitivity of Relative Gain Array for Processes with Uncertain Gains and Residence Times

Abstract The large scale industrial plants are often multivariable in nature. The preferred choice for the control of such plants is the decentralized multi-loop control system. The performance of a multi-loop control system depends strongly on the proper selection of control configuration. The most popular tool for control configuration selection is probably the steady-state relative gain array (RGA)”. It has later been extended to consider the effect of process dynamics. A very few attempts have been made towards the sensitivity of RGA analysis to model uncertainty and is majorly limited to the steady-state systems only. The aim of this paper is to gain insights into how process dynamics can affect control configuration decision based on RGA analysis in the face of model uncertainty. For the study, parametric uncertainty in gain and residence time of the process has been considered. Analytical expressions for worst-case bounds of uncertainty in steady-state and dynamic RGA are derived for two-input, two-output (TITO) plant models. A simulation example which has been used in several prior studies is considered here to demonstrate the applicability of the proposed approach. The simulink based closed-loop response has also been obtained to show the accuracy of results. The obtained bounds of uncertainty in RGA provide valuable information pertaining to the necessity of robustness and accuracy in the model of decentralized multivariable systems.

Industrial Pipe-Rack Health Monitoring System Based on Reliable-Secure Wireless Sensor Network

Energy and power industrial plants need to improve the health monitoring systems of their facilities, particularly high-risk facilities. This need has created a demand for wireless sensor networks (WSNs). However, for the application of WSN technology in large-scale industrial plants, issues of reliability and security should be fully addressed, and an industrial sensor network standard that mitigatesthe problem of compatibilitywith legacy equipment and systems should be established. To fulfill these requirements, this study proposes a health monitoring system of the pipe-rack structure using ISA100.11a standard. We constructed the system, which consists of field nodes, a network gateway, and a control server, and tested its operation at a large-scale petrochemical plant. The data obtained from WSN-based sensors show that the proposed system can constantly monitor and evaluate the condition of the pipe-rack structure and provide more efficient risk management.

Elemental Distribution and Mineralogical Composition of Ash Deposits in a Large-Scale Wastewater Incineration Plant: A Case Study

Release and transformation of incombustible inorganic substances during petrochemical industrial wastewater incineration form a large number of ash-forming species, which deposit and accumulate on the surfaces of heat transfer tubes, and can pose some severe operational problems, such as fouling, slagging, and even low heat transfer efficiency. Ash deposits characterization has been performed to investigate ash transformation and deposition behavior in a large-scale petrochemical industrial wastewater incineration plant using analytical techniques, including XRF, SEM–EDS, and XRD for elemental composition, morphology, and mineralogy. The results show that this volatile element has a dominated contribution to form varied ash deposits in a wide temperature range from 1000 to about 260 °C. Nickel and iron mainly in the oxide forms are transported by ash particles, have strong deposition propensity at high temperatures, and can be captured by the sticky initial deposition layer. Meanwhile, sulfur is enriched at low temperatures due to condensation, nucleation of alkali metal sulfates, and sorption of SO2/SO3. In the temperature range of 550–900 °C, eutectic mixtures which are temperature-dependent are formed in the molten phase and an “evolving” branched structure detected from SEM indicates the formation of a sintered deposition layer. Acidic salt sodium sesquisulfate is generated at low temperatures and should be responsible for the initiation of low temperature corrosion.

Industrial gamma-ray tomographic scan method for large scale industrial plants

In this paper, a tomographic scan method with fixed installed detectors and a rotating gamma-ray source system is presented to diagnose industrial plants, which were impossible to examine by conventional tomographic systems. Monte Carlo simulations had been performed for two kinds of phantoms. Lab-scale experiments with the same condition as one of the phantoms, have been carried out. Algebraic and statistical reconstruction methods were applied for the reconstruction of simulation and experimental data. The reconstruction results from different algorithms were compared. Simulation results showed that reconstruction from the photopeak counting measurement gave better results than the gross counting measurement, although the photopeak counting measurement had large statistical errors. The statistical algorithm gives better results for tomographic scan methods than the algebraic method for the simulation data. Simulation and experimental data showed that this work demonstrates the feasibility of the fixed detection and rotating source system for scanning of industrial plants. Those results appear to be promising for industrial tomographic applications, especially for petrochemical industries.