Manufacturing enterprises are changing the way they behave in the market to face the increasing complexity of the economic, socio-political and technological dynamics. Manufacturing products, ...processes and production systems result in being challenged by evolving external drivers, including the introduction of new regulations, new materials, technologies, services and communications, the pressure on costs and sustainability. The co-evolution paradigm synthesises the recent scientific and technical approaches proposed by academic and industrial communities dealing with methodologies and tools to support the coordinated evolution (
co-evolution) of products, processes and production systems. This paper aims at reviewing and systemising the research carried out in the field of manufacturing co-evolution with a particular focus on production systems. An introductory investigation of various industrial perspectives on the problem of co-evolution is presented, followed by the description of the co-evolution model and the methodology adopted for framing the existing scientific contributions in the proposed model. Then, the core part of the work is presented, consisting in a systemised analysis of the current methodologies dealing with co-evolving product, process and system and a description of problems that remain unsolved, thus motivating future research strategies and roadmaps.
The impurities CO2 and H2S in natural gas (NG) are recognized as major contaminants that exacerbate economic, operational, and environmental losses. Generally, these undesirable impurities are ...removed using well-established amine-based absorption methods. However, typical methods in this category are cost-intensive, primarily due to their high operating and maintenance costs. The ionic liquids (ILs) are emerging as alternative solvents owing to their lower regeneration costs and non-flammable nature. However, ILs could not attain a significant attention from practitioners due to the lack of effective communication between industry and academia. In this context, a comprehensive review and analysis of specific ILs that can simultaneously remove H2S and CO2 is proposed. This article highlights the major challenges and issues associated with various acid gases removal approaches, particularly IL-based absorption techniques. Recent developments toward solving the major issues associated with absorption using ILs are assessed to highlight areas for further improvement. The acid gas solubility data for ILs are analyzed to evaluate the feasibility and associated major constraints for large-scale process designs using commercial process simulators. Furthermore, the fundamentals for the process systems engineering-based investigations using ILs are also highlighted and evaluated. This study concludes that ILs have the potential to completely replace conventional solvents, have synergistic effects in terms of energy savings, and provide feasible solutions to maintenance-related issues.
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•Challenges in adopting ionic liquids as acid gas removal solvents are assessed.•Recent developments using ionic liquids for purification of natural gas are reviewed.•Solubility data for acid gases in ionic liquids are tabulated.•Process system engineering aspects for selecting ionic liquids are considered.•Current and future perspectives on commercial-scale adoption of ionic liquids are commented upon.
•Decarbonizing the steel industry is critical to reaching the goal of global net-zero carbon emissions by 2050.•Encourage the development and implementation of technologies that can reduce carbon ...emissions significantly in steel manufacturing processes.•Process system engineering perspective for analyzing the critical aspects of steel decarbonization.•To develop and implement energy management systems that optimize energy usage and reduce carbon emissions.
Decarbonization of steel manufacturing requires policies to reduce carbon emissions through technology development, renewable energy use, carbon pricing mechanisms, research and development, circular economy practices, energy management systems, and collaboration between industry, government, and academia. This policy assertion seeks to encourage the development and implementation of technologies that can reduce carbon emissions in steel manufacturing processes, such as hydrogen-based steelmaking, carbon capture and utilization, and energy-efficient processes. Low-carbon technologies, renewable energy, a carbon price, material efficiency, and collaboration are key strategies to reduce carbon emissions in the steel sector. Low-carbon energy sources such as wind and solar can be used to power the steelmaking process, while carbon pricing can reduce industrial emissions. To reduce emissions, stakeholders from all stages of the value chain must collaborate to develop decarbonization strategies, such as funding R&D, exchanging knowledge, and offering carbon-cutting incentives. This review provides a conceptual design approach proposed for the successful analysis of steel decarbonization potential from a process system engineering perspective. Challenges and opportunities are also been highlighted with respect to energy, economics, and environmental aspect. Technologies still require more advancement in terms of operation and energy intensity as technical and economic aspects are found superior to conventional technologies.
•A Deep Neural Networks (DNN) model for fault detection and diagnosis is process.•A Kernel Principal Component Analysis is integrated to enhance accuracy and generality of the model.•The relative ...performance of the hybrid model is compared with conventional methods.•The DNNs results in reduced training times and increased classification accuracy.
The increased complexity of digitalized process systems requires advanced tools to detect and diagnose faults early to maintain safe operations. This study proposed a hybrid model that consists of Kernel Principal Component Analysis (kPCA) and DNNs that can be applied to detect and diagnose faults in various processes. The complex data is processed by kPCA to reduce its dimensionality; then, simplified data is used for two separate DNNs for training (detection and diagnosis). The relative performance of the hybrid model is compared with conventional methods. Tennessee Eastman Process was used to confirm the efficacy of the model. The results show the reduction of input dimensionality increases classification accuracy. In addition, splitting detection and diagnosis into two DNNs results in reduced training times and increased classification accuracy. The proposed hybrid model serves as an important tool to detect the fault and take early corrective actions, thus enhancing process safety.
Modern manufacturing systems are becoming increasingly complex, dynamic, and connected, and their performance is being affected by not only their constituent processes but also their system-level ...interactions. This paper presents an integrated modelling method based on a graph neural network (GNN) and multi-agent reinforcement learning (MARL) collaborative control for adjusting individual machining process parameters in response to system- and process-level conditions. The structural and operational dependencies among process machines are captured with a GNN. Iteratively trained with MARL, machines learn to adaptively control local process parameters, e.g., machining speed and depth of cut, while achieving the global goal of improving production yield.
•A literature review and analysis is presented related to safety knowledge development and safety education.•Better understanding of demographic and temporal evaluation of safety education is ...presented.•The shortcomings of the current curricula in imparting safety concepts to the students are discussed.•A unique of process safety engineers education through a concept titled Safety Education 4.0 is presented.
Safety education has found a place in the chemical engineering curriculum. However, its complete impact is yet to be realized. A part of this delay may be attributed to the gap between the concepts traditionally taught in engineering education and the tools required for the practice of engineering in the process industry. This gulf is only exacerbated by the advent of Industry 4.0, which relies heavily on emerging technologies, such as big data, automation, and machine learning. Therefore, a fundamental aspect that educators of process safety need to readdress is the following: “How can safety knowledge and competence among engineering graduates be enhanced for them to have a sustained impact on the safe design and operation of chemical processes during industrial practice?” In this context, the present work investigates the status of safety knowledge development and transfer from education to practice. A detailed analysis of demographic and temporal evaluation of safety education in specific engineering disciplines, and the shortcomings of the current curricula in imparting safety concepts to the students, are also discussed in detail. This perspective attempts to bridge the gap between academic training and the job requirements of process safety engineers through a concept titled ‘Safety Education 4.0’. In the proposed Safety Education 4.0 methodology, academics and industry personnel collaborate in teaching students safety principles through project-based learning (PBL). The compact modular format of PBL makes it flexible to be updatable on-demand process safety education.
In thermodynamics, it is essential to distinguish between state functions and process functions. The reason is that the simple compressible thermodynamic system is a bivariate-process system, and the ...change of internal energy, a state function, corresponds to two process functions, heat and work. Among the state functions in thermodynamics, entropy is a special one because it has to be defined through a process function, exchanged heat δQ, and a unique factor of integration, 1/T. In heat transfer, it is shown that Fourier's law and the differential equation of heat conduction are both relations of state quantities alone, and process quantities appear when an integration with respect to time is applied. Moreover, an incompressible heat conduction medium element without conversion between heat and work is a univariate-process system governed by a single variable, temperature. In this case, the change of the thermal energy (“heat content”) stored in the system, a state quantity as a function of T alone, corresponds to only one process quantity, the transferred heat. Therefore, on the one hand, it is unnecessary to strictly distinguish between state quantities and process quantities in heat transfer, and on the other hand, there is no need to use a factor of integration to prove entransy a state quantity in heat transfer. Thermodynamics and heat transfer are two parallel sub-disciplines in thermal science. It is incorrect to deny entransy as a state quantity in heat transfer by the uniqueness of the factor of integration for entropy in thermodynamics, and entransy has significant physical meaning in the analysis and optimization of heat transfer processes.
The focus of this work is on an interpretation strategy on what a Convolutional Neural Network (CNN) has learned for fault detection and diagnosis (FDD) in process systems. Frequency spectra of ...process variables obtained by Continuous Wavelet Transform (CWT) are adopted as input features. Then, a CNN structure is designed to represent the mappings from input frequency features to different operation conditions. The Layer-wise Relevance Propagation (LRP) strategy is utilized to gain the relevance of each frequency feature to the classification performance. The formulations of relevance propagation for 4 types of CNN layers are presented in detail. The relevance scores are then depicted in heatmaps, where the pixels’ colors denote the contribution degrees and the most significant frequency features are considered as the major bases that the CNN discriminates different operation situations. The proposed interpretation strategy is experimented on the Tennessee Eastman process benchmark. The testing results demonstrate the efficiency of the strategy in interpreting what the CNN has learned to distinguish normal or faulty conditions in the FDD task.
•Interpretation on what convolutional neural networks learns for classification.•Fault Detection and Diagnosis using the frequency spectra features.•Layer-wise relevance propagation formulations for two-dimensional convolutional layers.•Describing relevance of input feature map in heatmaps.•Finding the most significant spectra features for operation conditions classification.
A Global Optimization approach of membrane gas separation processes, based on a general process superstructure including a wide array of possible configurations, and solved by a Nonlinear Programming ...formulation is presented. The capacity of the proposed approach to provide optimal configurations at minimum separation cost is first validated by comparing the obtained solutions with those of a reference study in the domain. The optimization approach is then applied to the optimization of CO2 capture from blast furnace gas considering multistage processes with up to four membrane stages. The optimal process configuration and main process variables, upstream and downstream pressure and membrane area, are determined for processes with CO2 recoveries of 90%, 95% and 99% and N2 residual contents of 1%, 0.5% and 0.1%. The resulting separation cost is in the range of 29–45 EUR/ton CO2 based on a NETL type cost model. Two stage permeate cascades (enrichers) with retentate recycle are shown to be the optimal configuration for N2 residual contents down to 1% at any recovery and down to 0.5% at 90% recovery. For larger recovery or purity levels, three stage processes offered the lowest separation cost. Four stage processes offered no marked improvement over three stage processes.
•Improved Global Optimization algorithm for multistage membrane processes.•Variable pressure ratio for each stage.•Vacuum operation is allowed for each stage.•Novel, cost effective multistage solutions are proposed.
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