Process industry

Several publications have discussed the beneficial effects of Lean and Industry 4.0 and how these two concepts are transforming the manufacturing sector. However, few have discussed how the process industry benefits from these two concepts. This paper seeks to find out the level of impact Lean has had on the process industry so far and its readiness for the fourth industrial revolution (Industry 4.0). The author consulted research and case studies from high-impact articles and publications from credible journals in putting together the paper. The perspectives obtained from these publications were used to make specific recommendations for the process industry so that it can reap the maximum benefit from both Lean and Industry 4.0. We discovered that Lean and Industry 4.0 could complement each other, contrary to some viewpoints. In most cases, the introduction of Industry 4.0 technologies advanced the already existing Lean practices. Also, processing facilities found it easier to implement Industry 4.0 digitalization tools, like the Internet of Things (IoT), cloud computing, and augmented reality (AR). These tools would also serve as a gateway into the larger area of Industry 4.0. This is going to have an impact on the policies of manufacturing companies, particularly those in the processing subsector. They would need to rethink their strategies from now on and consider incorporating these technological solutions into their processes to support their Lean initiatives to remain competitive in a fast-growing manufacturing environment.

Decarbonisation of the industrial sector is a crucial objective in new technological developments responding to global challenges. Energy-intensive industry is one of the biggest pollutants and it is mostly supplied by fossil fuels. This paper presents the methodology for the assessment of electrified options for the process industry based on systematic process integration techniques. The graphical representation was used to analyse the physical processes of the industrial unit. The improved heat recovery and electrified utility targeting were performed by using the Grand Composite Curve and analysing the main process streams and distillation column system. The electrified thermal utility system uses electric steam boilers, water coolers, heat pumps, etc. The case study analyses the natural gas liquid processing and assesses electrified thermal utility. The initial process was simulated in a UniSim environment and obtained thermophysical properties of process streams were used for the analysis. The integrated electrified scenario presumes using a low-pressure steam boiler, water coolers, propylene coolers and 3 heat pumps. Heat recovery was increased by 4 times compared to the initial process and 100% electrified thermal utilities were used. The energy cost was reduced by 41% and the carbon dioxide emissions are reduced by 512,778 tons per capita compared to existing process when using renewable energy for electrified utility. The approach can be used for further development of industry decarbonisation options and electricity targeting in process industries.

Among digital technologies, Artificial Intelligence (AI) and Big Data (BD) have proven capability to support different processes, mainly in discrete manufacturing. Despite the fact that a number of AI and BD literature reviews exist, no comprehensive review is available for the Process Industry (i.e. cements, chemical, steel, and mining). This paper aims to provide a comprehensive review of AI and BD literature to gain insights into their evolution supporting operational phases of the Process Industry. Results allow to define the areas where AI/BD are proven to have greater impact and areas with gaps like for example the process control (predictive models) area, machine learning and cyber-physical systems technologies. The sectors lagging behind are Ceramics, Cement and non-ferrous metals. Areas to be studied in the future include the interaction between intelligent systems. humans and the external environment, the implementation of AI for the monitoring and optimization of parameters of different operations, ethical and social impact.

The interior relationship between Industrial Ecology, Eco-Efficiency and Industrial Symbiosis has been scarcely investigated in literature. We identify three main aspects linking the concepts, which are 'Actions', 'Stakeholders' and 'Value', and use them to drive the conceptual analysis. Considering the application and implementation, authors conduct a conceptual comparison between Eco-Efficiency and Industrial Symbiosis by using Industrial Ecology as the leading concept. A conceptual framework is developed to uncover the relationship of Industrial Ecology, Eco-Efficiency and Industrial Symbiosis, from a firm level perspective.

Problem statement and research aim: The term resilience has been published for decades to describe how different types of systems and environments respond to unexpected shocks. Most of these works address the subject of sudden disturbances such as natural disaster and its effects on the built environment. While there are few studies that have examined the effect of slow disturbances; Therefore, it is worth noting that in today's societies, the resilience of built environments has an active role in resilience of cities, and the life of each city depends on the life of the different spaces and environments in which it resides. The Spatial dimension of resilience is a new subject in field of architecture which indicates the continued vitality of spaces over time versus gradual and slow disturbances. In today's societies, spaces that are both designed and constructed without reading the text and responding appropriately and without considering the spatial capabilities are vulnerable to shocks and changes and lose their effectiveness quickly.In this regard, attention to spatial features and capabilities, although contributing to resilience to change, is not alone as the basis and sufficient structure in particular in the dimension of culture, history to examine the subject, hence the harmony of space with the context of design and utilizing contextual components is of the utmost importance. Therefore, contextualize approach can offer valuable ways in spatial resilience. Therefore, the purpose of the research is to identify effective components and spatial features with emphasis on contextualize approach. Thus, the research seeks to answer this question: 1- What is the level of resilience of Tabriz's historical bazaar space in terms of contextualist approach? And 2- How do the components and indicators play an effective role in achieving this resilience, relying on this approach?
Methodology: The present study is a case study that contains the grounded theory to guide the research process. In this research, the contextualist approach as the grounded theory was used to formulate the research program. This research is a descriptive-analytical research which aims to identify and search the factors of resilience spaces which theoretical framework were accomplished by extracting contextualism components by meta-analysis and spatial capabilities by analytical method based on the findings of the researchers. In the present study, the components of contextualization as an independent variable and space capabilities as a mediating variable and space resilience as a dependent variable of research and individual factors such as age, gender, marital status, education, economic status, location Residence and interval of referral are considered as controlling factors. In order to gather information, library study and field techniques have been used. Data collection tool in field technique is a questionnaire based on research variables and demographic characteristics of the respondents to assess the viewpoints of Tabriz historical market clients regarding space resilience. The target population of this study was selected by Cochran sampling with 95% confidence level of 357 individuals. Data were analyzed using SPSS and lisrel software using mean test, structural analysis and multivariate linear regression. In order to explain the present situation, prioritize and rank the components and indices of the mean test and box plot diagram, examine the relationships between variables and the direct and indirect effects of path analysis and measure the effect of indices on variables of regression test have been used.
Findings: In this study, according to the purpose of the research, the findings showed that among the individual factors, three factors such as age, level of education and economic status affect the level of contextualism. According to the results of the mean test, the resilience of the historical bazaar of Tabriz is optimally based on spatial capabilities and contextualism components, and the historical context and flexibility capability are at their best. In examining the score and priority of contextualism indices, the first and highest mean scores are related to the indices of structural coherence, adaptation to religion and historical species and conformity of the body with the climate, respectively. And the highest ratings and average ratings have been found in reviewing space capability indices, in indices of polyvalence, social capital, user trust partnerships, and permanent conservation and restoration. The results of structural analysis and regression indicate that there is a significant relationship between all variables of research. According to the results of the path analysis of all the governing relationships in the structural model except for the component of flexibility is significant. In this regard, in contextualism variable the highest path coefficient is related to socio-cultural and climatic-environmental component (0.70) and the least path coefficient is related to physical component (0.52). In the spatial capability variable, the adaptability and reactivity components have the highest path coefficient (0.77). Therefore, there is a direct and significant relationship between the capabilities (adaptability, variability and reactivity) of space and the components (physical, cultural, historical and environmental) of contextualization with spatial resilience, also the findings indicate that there is no significant relationship between space flexibility and space resilience. And the greatest effect of contextualization on resilience is through spatial capabilities, thus the indirect effect of contextualization on resilience is greater than its direct effect. The environmental and cultural context and the adaptability and reactivity of space have the most impact. Among the contextualism indices, the most influential ones relate to indices of visual cohesion of the physical context, conformity with local values of the cultural context, adaptation to historical species of the historical context, and conformity of the body with the climate of the environmental context and in Among the indices of spatial capabilities, the greatest impact is related to reconfiguration indices of adaptability capability, changing patterns and processes of variability, and the social system diversity index of reactivity. Therefore, contextualized space, by utilizing the capabilities of space, can survive over a period of time without loss of identity by maintaining performance against disturbances.
Conclusion: Based on the results, utilizing spatial capabilities with the emphasis on contextual components is effective in resilient spaces. There is a direct and significant relationship between space capabilities except flexibility and space resilience, and the greatest effect of contextualism components on space resilience is through space capabilities, so the indirect effect of contextualism on resilience is more than its direct effect. The environmental and cultural context and the adaptability and reactivity of space have the greatest impact. However, the purpose of this research is simply to identify the causes and not to design strategies within the scope of this research and to develop it requires further research. Therefore, it can be concluded that considering the conditions of the context and these capabilities over time can be a good step towards improving future resiliency of space. The achievement of this research is to present the factors that promote the resilience of space that can lead to a new way of designing new spaces and assessing and explaining the resilience strategies to promote urban resilience.

In this work, the solution to the optimization of process operations in the leather industry is studied in its full complexity. The interest of this study is twofold: for one thing, it shows how scheduling optimization problems can be solved at the industrial scale level, and on the other hand it represents an step forward in creating a general framework to deal with resource constrained acyclic production scheduling problems for practically sized problems. The optimization procedure uses hierarchical decomposition of the global MINLP problem into subproblems, that take into account the acyclic problem present in the short-term periods, which implies eventual task rescheduling in order to minimize loses in resources resulting from the desired uniform production loads. Results from several case studies are discussed. In all cases validation of the results obtained has been made through the extensive use of actual plant data and the experience provided by leather manufacturing experts.

This paper is Part 3 in a three-part series of papers addressing operational techniques for applying mass integration principles to design in industry with special focus on water conservation and wastewater reduction. The presented techniques derive from merging U.S. and Danish experience with industrial applications of process integration, as a result of a recently established cooperation between the

The project "Multiphase flows in process industry (ProMoni)" 1.1.2001– 30.4.2004 was a research consortium carried out jointly by seven research groups from VTT, University of Jyväskylä, Tampere University of Technology, Åbo Akademi University and University of Kuopio. It included modeling, development and validation of numerical methods as well as development of new experimental techniques for multiphase flows found in process industry. The primary fields of application are in fluidized beds and in various processes found in the paper and pulp industry. This extensive final report of the ProMoni project includes results from experimental and numerical research of bubbling fluidized beds indicating that modelling produces the bed behaviour realistically and the simulations can be used in developing approximative macroscopic two-phase models. A new gas-solid drag correlation model was successfully used within Fluent CFD code to simulate the typical flow structures in a circ...

The topic of domino effects in chemical industry has started to gain more attention in risk analysis and safety assessment researches over the past two decades. The popularity of the topic is partly due to the occurrence of catastrophic domino effects, e.g., LPG-induced domino effects in Mexico City in 1984, and partly due to legislations, Seveso Directives, mandating the owners and managers of chemical plants to take the likelihood of domino effects into account when contemplating the prevention/mitigation of major accidents in their plants. The present study aims to take advantage of the-state-of-the-art bibliometric analysis tools to investigate the trend, and the geographical and authorial distributions of scientific papers, mostly published in peer-reviewed journals and excluding books and technical reports, around the globe. The result of this study can be used by researchers in the identification of the most influential research institutes and authors contributing to the domain of domino effects in chemical industry.

In the last few years, several events have highlighted the risk of possible major accidents triggered by terrorist attacks within chemical and process facilities. Due to the increased attention for security issues, an optimal allocation of security measures, including related cost issues, becomes progressively more important. Despite the existence of economic models for supporting decision-making processes in general, for instance costbenefit and cost-effectiveness analyses, no specific economic models are available in the domain of operational security (including counter-terrorism decision-making) to be applied within the chemical and process industry. In this study, a novel method for cost-benefit and cost-effectiveness analysis for the allocation of physical security measures was elaborated and tested using a case study. Starting from a site-specific analysis of the baseline physical security system performance, the model allows comparing and evaluating the costs of different security upgrades with the (hypothetical) benefits related to the avoided losses. The approach developed enables selecting economically feasible security measures, or a combination of such measures with a maximum net present value, within the budget constraints of a chemical plant.

This paper addresses the possibilities of implementing Computer Integrated Manufacturing in the process industry, and the chemical industry in particular. After presenting some distinct differences of the process industry in relation to discrete manufacturing, a number of focal points are discussed. They identify the adaptations to be made to a conventional CIM system, so that it will be applicable to the process industry. Interviews with managers of six chemical firms indicate that the process industry may benefit from a new CIM approach to management thinking.

Abstract:The operation of modern day process plant cannot be visualized without the smart looking Distributed Control System (DCS) situated in control rooms. Process plant automation has evolved from pneumatics to electronics’ to DCS. Use of DCS leads to various advantages like the overall optimization, ease of operation and maintenance tasks, easy monitoring of more plant parameters and ensure tighter control on them. The field instrumentation and control system have to be integrated properly and must function optimally to achieve such a goal.

Risk assessment is the tool for maintaining perfect safety management systems and aiding sustainable process design, with hazard identification as the critical step. This step can be executed by past accidents analysis (PAA) to achieve the mentioned objectives. Despite of available analyses, the recurring of accidents identifies the shortcomings in PAA and requires a detailed examination as reported in this review. The intensified exploration of accident information will strengthen both the safety management system at existing facilities and process designing in terms of sustainability.

In competitive environment lean manufacturing is necessary in every industry. Lean production is a standard manufacturing mode of the 21st century. Lean manufacturing have various synonyms like lean management, lean production, It is very use full techniques for reduce the flow of non value added time. Lean manufacturing is a philosophy to reduce the time between customer order and products are ready for the delivery by eliminating waste. Waste (muda) is a serious problem in to the industry it is creates a non value added services and poor quality of the product. Waste are seven type occurs in industry 1) transport,2) inventory,3)motion,4) waiting,5) over production,6) over processing,7) defect. It is eliminate with the help of lean manufacturing. It is an integrated system that gives production of goods/services with minimum buffering costs. This paper gives the literature survey on various type of industry to apply the lean manufacturing, like apply in to automobile industry, and ...

The paper attempts to identify the key issues in the implementation of lean manufacturing system (LMS) in the United Arab Emirates. The paper also discusses the various outcomes expected from the successful implementation of LMS. Extensive literature review has been done to find out the various critical issues and the key issues selected for the study.

The paper attempts to identify the key issues in the implementation of lean manufacturing system (LMS) in the United Arab Emirates. The paper also discusses the various outcomes expected from the successful implementation of LMS. Extensive literature review has been done to find out the various critical issues and the key issues selected for the study.

"Lean and Efficiency: Mastering the Tools of Lean Management" is a book that provides a step-by-step guide to understanding and implementing lean practices in various industries. It covers the fundamentals of lean management, including its history, evolution, and core principles. The book also discusses value stream mapping, lean tools and techniques, and their application in specific industries. Chapters cover lean manufacturing, service, just-in-time inventory management, total productive maintenance, set-up time reduction, quality control, and employee engagement. The book emphasizes the importance of employee engagement and continuous improvement in lean management, empowering employees to create a culture of continuous improvement and innovation. Overall, "Lean and Efficient: Mastering the Tools of Lean Management" is an essential resource for individuals and organizations seeking to optimize processes, reduce waste, and achieve operational excellence.

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Background: The process industry is known for its hazardous and complex nature, and thus, hazard identification and risk management are crucial to ensure safety. Implementing hazard identification methods and incident modeling concurrently is essential to prevent adverse events. This study aims to identify potential hazards in a sulfur recovery plant from an environmental perspective using the Hazard and Operability (HAZOP) study method and to model the identified hazards using the Bowtie technique. Method: This descriptive cross-sectional study involved five research steps to gather necessary information on the sulfur recycling unit. The HAZOP study was utilized to identify process hazards, which were subsequently modeled using the Bow-tie method in Bow-tie Pro software. The statistical analysis and important variable values of these two methods were described. Results: The findings of the study conducted on the sulfur recycling unit revealed the identification of 10 nodes, 95 deviations, 158 causes, 186 consequences, 95 safeguards, and 92 recommendations through the HAZOP study. Additionally, the Bow-tie study recognized 19 top events, 19 threats, 90 controls, 69 consequences, and 162 escalation factors. Conclusion: The results demonstrate that any faults or deficiencies in control devices can significantly impact the consequences of environmental pollution in process industries. However, efficient monitoring of process deviations and the implementation of recommended measures can effectively eliminate or reduce such impacts.

Being able to quickly integrate new equipment and functions into an existing plant is a major goal for both discrete and process manufacturing. But currently, these two industry domains use different approaches to achieve this goal. While the Module Type Package (MTP) is getting more and more adapted in practical applications of process manufacturing, so-called skillbased manufacturing approaches are favored in the context of discrete manufacturing. The two approaches are incompatible because their models feature different contents and they use different technologies. This contribution provides a comparison of the MTP with a skill-based approach as well as an automated mapping that can be used to transfer the contents of an MTP into a skill ontology. Through this mapping, an MTP can be semantically lifted in order to apply functions like querying or reasoning. Furthermore, machines that were previously described using two incompatible models can now be used in one production process.

Iron-and-steel making is a carbon-intensive industry and responsible for about 8% of global CO2 emissions. Meeting CO2 reduction targets is challenging, since carbon is inherent in the dominating production route in blast furnaces. Long-term plans to phase out carbon and change production technique are under way, such as iron ore reduction with hydrogen[1][2] won from renewable energies or electro winning[3], however unlikely to be implemented at scale before 2040 [4]. Until a transition to such technologies is completed, carbon leakage will remain to be a threat to steel industry inside EU ETS system. CCS remains an option for steel industry to comply with reduction targets and meet rising allowance (EUA) prices, currently above 20 €/t. Most studies on CCS propose a capture rate of ≥ 90 %[5–7], however, CCS could be considered as a part of a series of measures (e.g. fuel change, energy efficiency measures) that together achieve a significant reduction in CO2 emissions until a carbo...

This study aimed to evaluate the safety resilience in process industries with a passive defense approach in South Pars Phase 19 refinery. This descriptive-analytical study was conducted in the period 2018 to 2020. The study sample included sweetening unit of South Pars Phase 19 gas refinery. Based on the three main components of probability, severity, and preparedness, a semi-quantitative model used for evaluating safety resilience in process industries. Data analysis performed using SPSS v.25 and AMOS v.24 software. 131 sources of danger were identified. 21.4% of the identified hazard elements are in the weak threat range, 59.5% are in the moderate threat range, and 19.1% are in the severe threat range. The maximum and minimum values ​​of the present study's resilience index were 500 and 10, respectively, and are related to the risk element of utility cut-off and instrument cut-off. The results showed that the resilience index is generally predictable up to 91%. The three components of probability, severity, and preparedness predicted the resilience indicators with values ​​of 0.39, 0.78, and 0.37, respectively. The findings indicated that severity index is the most crucial component in determining the vulnerability and resilience parameters of the system due to the nature of the process and critical parameters in refineries. Therefore, resilience assessment, attention to the severity of possible consequences, and the establishment of different protective layers to reduce the amplitude and severity of damage caused by catastrophic events is the most vital principle to increase process systems' resilience levels.

Improving efficiency in Manufacturing Execution Systems (MES) engineering and ensuring interoperability requires a tool or a tool suite, which handles the desired (heterogeneous) data representations or better an integrated (homogeneous) data representation-a data format aiming at interoperability. CAEX (Computer Aided Engineering Exchange, [1]) is one possible example of such a format. Its objective is a tool-independent description of plant planning data. CAEX is also used as a top-level format for AutomationML [2], [3]. To simplify the usage of this complex data exchange format, the authors developed various useful assistance tools which help users to work with it. These tools for transformation of arbitrary plant engineering data, their background and methodology will be presented in this contribution.

A set of key performance indicators (KPIs) for manufacturing operation management is introduced in the ISO 22400 standard. However, KPIs are only defined at a high abstraction level and this hampers the standard's practical adoption. In this paper, a framework is introduced in order to solve this weakness. Starting from the analysis of the relevant measurements used in the KPIs' formulas, a classification model is introduced defining three possible application scopes-work orders, work units and production orders-to analyze each basic element of the standard. As a result, the KPIs defined in the ISO 22400 standard are more precisely specified and, as a consequence, also suitable to be straightforwardly implemented in performance measurement systems.

Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization goes beyond the traditional aim of capacity maximization, contributing also for organization's profitability and value. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of maximization. The study of capacity optimization and costing models is an important research topic that deserves contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization's value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency.

Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization goes beyond the traditional aim of capacity maximization, contributing also for organization's profitability and value. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of maximization. The study of capacity optimization and costing models is an important research topic that deserves contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization's value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency.

A set of key performance indicators (KPIs) for manufacturing operation management is introduced in the ISO 22400 standard. However, KPIs are only defined at a high abstraction level and this hampers the standard's practical adoption. In this paper, a framework is introduced in order to solve this weakness. Starting from the analysis of the relevant measurements used in the KPIs' formulas, a classification model is introduced defining three possible application scopes-work orders, work units and production orders-to analyze each basic element of the standard. As a result, the KPIs defined in the ISO 22400 standard are more precisely specified and, as a consequence, also suitable to be straightforwardly implemented in performance measurement systems.

Modern industrial systems are growing day by day and unlikely their complexity is also increasing. On the other hand, the design and operations have become a key focus of the researchers in order to improve the production system. To cope up with these chellenges, the data-driven technique like principal component analysis (PCA) is famous to assist the working systems. A data in bulk quanitity from the sensor measurements are often available in such industrial systems. Considering the modern industrial systems and their economic benifits, the fault diagnostic techniqes have been deeply studied. For example, the techniques that consider the process data as the key element. In this paper, the faults have been detected with the data-driven approach using PCA. In particular, the faults have been detected by using and statistics. In this process, PCA projects large data into smaller dimensions. Additionally it also preserves all the important information of process. In order to understand the impact of the technique, Tennessee Eastman chemical plant is considerd for the performance evaluation.

This paper addresses the design of a reconfigurable mobile manipulator consisting of a mobile base and a collaborative serial robot. The robotic system is meant to work in an industrial environment and perform different logistic tasks. Unlike commercial solutions, the mobile base and the anthropomorphic arm are free to decouple and work separately as two different entities, thus optimizing working times and maximizing the hardware utilization ratio. The proposed mobile manipulator is equipped with an automatic braking system to ensure safety and stability during manipulation, a lifting system that allows the robotic arm to work at different heights, and a spatial referencing process to compensate for positioning error of the mobile base. An illustrative working cycle is implemented in an industrially relevant environment to test all features and show potentialities, in terms of flexibility and reconfigurability, of the presented solution.

In recent decades, machine learning (ML) techniques have been effectively applied for industrial process monitoring to assure safety and high-quality yield. Traditional process fault detection, identification, and diagnosis (FDI&D) approaches are insufficiently smart to address the modern complex challenges of real-time industrial chemical processes. The detection and diagnostic resolution of the traditional monitoring approach are less robust, inefficient, and produce the wrong interpretation of actual fault information. These approaches are based on a single-scale fault illustration and cannot effectively address multiple fault depiction roots. This study introduces a novel ML-aided methodological framework for industrial and manufacturing monitoring systems. The proposed ML framework is developed using Principal component analysis (PCA), Shannon information entropy (IE), wavelet transformation (WT), and signed directed graph (SDG). It includes fault detection, identification, and diagnostic propagation root-path interpretation to address the safety challenges of modern, real-time industrial chemical processes. The proposed methodological framework is validated using the Tennessee Eastman process (TEP) benchmark to highlight their performance and efficiency. The results of this study determined that the new proposed approach is more efficient in terms of accuracy, robustness, and actual propagation root cause than traditional techniques. It has a high fault detection rate (FDR), low fault alarm rate (FAR) that identifies and recognizes the actual faulty-correlated variables to establish the SDG model framework for determining the actual diagnostic propagation root path. It initially enables operators to react to unusual incidents, ensuring industrial safety, minimizing economic loss, and avoiding disasters.

With the aim of exploring explosion characteristics of methane-air explosive mixtures in a ducted vessel, a 20 litre spherical vessel connected with a 2813 mm long duct was employed. The experimental setup was comprised of a wafer check valve, which kept the methane-air mixture initially confined and opened at the time of explosion. The system introduced turbulence to the gas mixture during operation and pyrotechnic igniters were employed in the investigation. This approach assisted to obtain data that can be correlated with real world ducted explosion accidents where the explosion initiates in the presence of strong ignition energies and in turbulent states of methane-air mixtures. This study shows that the explosion severity can be very high in the turbulent field of methane-air mixture and in the presence of strong ignition energies. The pressure rise in the vessel and the flame speed along the length of the duct were found to be higher in the present study when compared to data obtained with quiescent mixtures of methane-air and low ignition energies. The impact of the duct length and pyrotechnic igniters' energy on reduced peak explosion pressure was characterised. The rate of pressure rise, a parameter linked to the burning rate, increased from the ducted to the vented configurations of the explosion test units.

This paper deals with aspects related to the improvement of the design of a logistics equipment (conveyor belt). Conveyor belts are very important logistics equipment within the logistics systems and aim at the controlled and safe transport of products, requiring as little as possible the human operator and guaranteeing their safety at the workplace. The necessity of the study derived from the fact that in the industry there are different situations when the conveyors have to be customized, depending on the activity carried out. In addition, a flexible production line requires "flexible" conveyors, that is to say adaptable to the required situations: transport capacity, ground band height, control panel layout, special conditions (smoke, dust, humidity ... etc). In order to establish the ergonomic design parameters, we applied concepts from Value Analysis and RULA analysis. The simulation parameters were used to simulate an Artificial Neural Network, which, after training, would be able to provide design requirements for new situations.

Process industry has a need to increase product quality while also reducing operating costs and environmental footprint. However, a plant comprises of a complex set of dynamic processes, as well as human operators that control the processes. The best improvement is achieved by an extensive intervention of both aspects. In practice, human factor aspects have often a marginal role in carrying out a technical project. This has not been the case in the present, EU-funded project. The systematic inclusion of human factors perspective requires parallel and collaborative proceeding of technical development and human factors. The flexible approach is central as the human matters not only affects technical development but is also involved with the usage context of the new technical solution.

Traditionally operated manufacturing industries are facing problems like low productivity, longer production lead time, high rework and rejection, poor line balancing, low flexibility of style changeover etc. These problems were addressed in this study by the implementation of lean tools like 5S, continuous flow, quick change over, total productive maintenance. After implementation of lean tools, results observed were used to assess the leanness of the plant floor. The key benefit of assessment is to identify the current state of production plant. And also to track the overall performance of the plant and it is evident that the leanness level can be improved by focusing on the key areas. This study is conducted in the steering knuckle manufacturing section in a manufacturing company. It includes time studies and current value stream mapping. Lean assessment was carried out using questionnaire which helped in diagnosing the lean status.

This paper presents an overview of different methods used in what is normally called AI-methods today. The methods have been there for many years, but now have built a platform of methods complementing each other and forming a cluster of tools to be used to build “learning systems”. Physical and statistical models are used together and complemented with data cleaning and sorting. Models are then used for many different applications like output prediction, soft sensors, fault detection, diagnostics, decision support, classifications, process optimization, model predictive control, maintenance on demand and production planning. In this chapter we try to give an overview of a number of methods, and how they can be utilized in process industry applications.

Management of Change (MOC) is a process for evaluating and controlling modifications to facility design, operation, organisation, or activities. It is one of the most important elements of Process Safety Management (PSM). In chemical process industries (CPI), MOC is required to ensure that safety, health and environment are controlled. In recent years, the number of accidents related to MOC failure is significant and caused by the lacks of MOC management, organisation safety culture, design failure, incompetency, human factor and etc. From the accident statistics published by Chemical Safety and Hazard Investigation Board (CSB-US), European Major Accident Reporting System (EMARS-European), Failure Knowledge Database (FKD-Japan) and Accident Reporting Information Analysis (ARIA-France), MOC contributes significantly to the occurrence of accidents and its percentage contribution to accident rate is not decreasing over the past 20 years. In this paper, the contribution of MOC failure t...

Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization goes beyond the traditional aim of capacity maximization, contributing also for organization's profitability and value. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of maximization. The study of capacity optimization and costing models is an important research topic that deserves contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization's value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency.

Lean manufacturing or lean production, often simply "lean", is a systematic method for the elimination of waste ("Muda") within a manufacturing system. The concept lean also comprises waste created through overburden ("Muri") and the ones created through unevenness in work loads ("Mura"). This has led to this paper review on managing Lean Principles applicability and implementation in the mining industry in Ghana. This paper reviewed both theoretical and empirical literature on lean applicability in the mining industry in Ghana. The paper seeks to explore variables, which may be considered in lean applicability and implementation in the mining industry on: principles that are identified with the lean strategies application; assess the various tools that are adopted for lean implementation; determine how the lean management strategies are applied in the mining industry; explore the main benefits derived by the players in the mining industry for adopting lean strategies; and finally, identify the challenges that confront mining companies in their quest of adopting lean strategies. It is concluded that leanness as a management principle can successfully be applied to any sector of the economy. Base on such premise (postulation), it is worth noting that there is evidence of great advances regarding leanness as a management practice in the mining industry across the globe.

Background: The process industry is known for its hazardous and complex nature, and thus, hazard identification and risk management are crucial to ensure safety. Implementing hazard identification methods and incident modeling concurrently is essential to prevent adverse events. This study aims to identify potential hazards in a sulfur recovery plant from an environmental perspective using the Hazard and Operability (HAZOP) study method and to model the identified hazards using the Bow-tie technique.
Method: This descriptive cross-sectional study involved five research steps to gather necessary information on the sulfur recycling unit. The HAZOP study was utilized to identify process hazards, which were subsequently modeled using the Bow-tie method in Bow-tie Pro software. The statistical analysis and important variable values of these two methods were described.
Results: The findings of the study conducted on the sulfur recycling unit revealed the identification of 10 nodes, 95 deviations, 158 causes, 186 consequences, 95 safeguards, and 92 recommendations through the HAZOP study. Additionally, the Bow-tie study recognized 19 top events, 19 threats, 90 controls, 69 consequences, and 162 escalation factors.
Conclusion: The results demonstrate that any faults or deficiencies in control devices can significantly impact the consequences of environmental pollution in process industries. However, efficient monitoring of process deviations and the implementation of recommended measures can effectively eliminate or reduce such impacts.

Ontologies provide an effective way for describing and using knowledge of a specific domain. In engineering workflows the reusability and quick adoption of knowledge is needed for solving several tasks in efficient ways. Engineering data is mostly structured in hierarchical documents and exchange formats, but is not represented in ontologies. Therefore a connection between engineering data and the knowledge in ontologies is needed. In this article we present a bridge concept for connecting engineering data with an OWL-based ontology. For this we use an example ontology containing security knowledge of automation systems.

Lean production tools and principles, which stem from Toyota Production System, have been applying also in the healthcare industry. In the last decade many organisations have tried to adopt Lean within their wards, departments and patient's routings. However, Lean in the healthcare, especially in the public healthcare, needs to be implemented with some adjustments. This paper wants to contribute to the debate concerning lean implementation in the public healthcare, in particular analysing the possible benefits that can be achieved, the pitfalls and the adjustments to be made. The paper is based on a qualitative inquiry through the interview to three managers who belong to three large Italian public hospitals. In these hospitals, in the last years, many processes have been affected by lean applications. Findings show that tools such as Value Stream Map (VSM), 5S and Single Minute Exchange of Die (SMED) and Kanban can be applied like so in the manufacturing industry. The main achievable benefits are bound to the lead time reduction of the patient's routing, cost reduction and patient's satisfaction. It is not that easy to measure the economic and financial benefits, moreover pitfalls and difficulties are sometimes in ambush. Problems related to senior managers' commitment as well as staff's involvement and motivation have to be taken into serious consideration.

He received his Ph.D. in Operations Management from The University of Texas at Arlington, and holds an MBA from the Lahore University of Management Sciences along with a B.Sc. in Mechanical Engineering from the University of Engineering and Technology Lahore, Pakistan. His teaching interests include Operations Management, Analytics and Project management. Khurrum has published in several leading journals including;

It is crucial to provide operators supports for operation planning in complex process plants. This paper presents an operation searching approach used for synthesis of valve and pump operations to establish specific pipe routes. Instead of modeling pipeline fragments, a functional modeling methodology called Multilevel Flow Modeling (MFM) is adopted to represent plant knowledge. Relying on causality feature of MFM, one can identify required state changes of specific functions in the model, and accordingly operations. Since relationships between functions, components, and operations are independent to the modeling object, these generic principles can be implemented into a rule-based reasoning system for inferring operational conditions of pipe routes, namely, operations. An example in the literature has been used to demonstrate the presented approach and application of the software.