Search

This research is intended to examine the adoption of the Quality by Design (QbD) approach within a process industry setting, thereby leading to sustainable performance. Specifically, the purpose of this study is to explore the systematic integration of QbD principles into the design and control of distillation column controllers to improve product quality, operating efficiency, and sustainability. The study analyzes process variables and develops a robust system by leveraging statistical methods, quality management tools, and chemical and control engineering expertise. It is found that effective deployment of QbD methodology in process engineering requires a robust and systematic approach. Additionally, we determined that Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQA) are important components for effective deployment and sustainment of QbD to achieve Sustainable Development Goals for the industries. Moreover, it is observed that including noise and control parameters is essential during the project's design phase. This study offers a systematic approach to implementing QM in industrial process design. The study highlights the potential of QbD in control engineering to enhance industrial processes and contribute to sustainability. [ABSTRACT FROM AUTHOR]

Increasingly, pressing sustainability issues including the rise in greenhouse gas (GHG) emission rates, climate change‐related vulnerabilities, and natural resource depletion have propelled companies to transition from a linear economy to a circular economy (CE). While circular business models are gaining currency in the manufacturing sector, empirical research on CE transition in the continuous process industry in developing economies is scarce. Accordingly, the purpose of this study is to investigate the drivers and barriers of CE adoption in the textile industry of Pakistan. To this end, we utilized a qualitative methodology, and a total of 22 semi‐structured interviews were conducted with consultants and senior corporate managers working in the textile sector. Building on the natural resource‐based view (NRBV) and institutional theory, the findings revealed various internal drivers (resource efficiency‐related, organization‐related, and research and innovation‐related factors) and external drivers (market, regulatory, and societal factors) for CE transition. Additionally, the findings demonstrated internal barriers including behavioral, technical, and economic issues, and external issues such as customer and brand‐related barriers, regulatory and policy‐related barriers, as well as supply chain‐related barriers hindering the adoption of CE. We argue that it is one of the early studies to utilize the NRBV and institutional theory to examine the drivers and barriers and provide novel insights into the CE transition in the textile process industry in a developing economy. The findings can assist academics, consultants, practitioners, and policymakers to understand and promote CE as a sustainable strategy in the textile process industry. [ABSTRACT FROM AUTHOR]

The introduction of Self-X capabilities into industrial control offers a tremendous potential in the development of resilient, adaptive production systems that enable circular economy. The Self-X capabilities, powered by Artificial Intelligence (AI), can monitor the production performance and enable timely reactions to problems or suboptimal operation. This paper presents a concept and prototype for Self-X AI in the process industry, particularly electric steelmaking with the EAF (Electric Arc Furnace). Due to complexity, EAF operation should be optimized with computational models, but these suffer from the fluctuating composition of the input materials, i.e., steel scrap. The fluctuation can be encountered with the Self-X method that monitors the performance, detecting anomalies and suggesting the re-training and re-initialization of models. These suggestions support the Human-in-the-Loop (HITL) in managing the AI models and in operating the production processes. The included Self-X capabilities are self-detection, self-evaluation, and self-repair. The prototype proves the concept, showing how the optimizing AI pipeline receives alarms from the external AI services if the performance degrades. The results of this work are encouraging and can be generalized, especially to processes that encounter drift related to the conditions, such as input materials for circular economy. [ABSTRACT FROM AUTHOR]

The industrial sector is the primary source of carbon emissions in China. In pursuit of meeting its carbon reduction targets, China aims to promote resource consumption sustainability, reduce energy consumption, and achieve carbon neutrality within its processing industries. An effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering technology. This strategy aims to attain an optimal solution for material performance, resource consumption, and environmental impact. In this study, five types of technologies were considered: raw material replacement, process reengineering, fuel replacement, energy recycling and reutilization, and material recycling and reutilization. The meaning, methodology, and development status of life cycle engineering technology abroad and domestically are discussed in detail. A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption, carbon emissions, product performance, and recycling of secondary resources in a manufacturing process. This coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry, as an example. The results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

This study investigates the seismic behaviour of a special mixed reinforced concrete-steel structure that supports an oil refinery reactor. The structure is 64.90 m tall and consists of three parts: (a) a reinforced concrete frame basement; (b) a steel braced frame that supports the oil reactor and (c) the steel reactor itself. A three-dimensional model of the structure is created to perform static non-linear (pushover) analyses in order to obtain the capacity curves and understand the overall inelastic behavior of the structure. The results of the pushover analyses reveal that the structure exhibits similar inelastic behavior in both horizontal directions and satisfies the capacity design principles. The structure exhibits limited ductility considering the fact that has been designed with a behavior factor of q = 1.5 and primary damages are expected mainly in concrete members. Subsequently, dynamic non-linear time-history (NLTH) analyses are performed utilizing the three translational components of three seismic motions recorded during past earthquakes. These results involve: (i) the maximum values for displacements, accelerations and base shears; (ii) the maximum stresses at critical points of the oil refining reactor and (iii) the formation of plastic hinges at columns, beams and braces of the structure. Contrary to pushover analyses, NLTH analyses revealed the development of plastic hinges, hence seismic damage, that do not follow the desirable formation pattern. Moreover, the accelerations and displacements observed are expected to cause failure of the piping and mechanical equipment, while local failure of the high-stress areas of the shell of the reactor may be possible. Localized strengthening might be necessary to avoid repair works and downtime after such seismic event. [ABSTRACT FROM AUTHOR]

Industrial pressure relief valves must function reliably and effectively to protect pressurized systems from excessive pressure conditions. These valves are essential safety devices that act as cushions to protect piping systems, equipment, and vessels from the risks of high pressure, which can cause damage or even explosions. The objectives of this study were to minimize valve failures, decrease the number of rejected valves in the production line, and enhance the overall quality of pressure relief valves. This work introduces an integrated quality improvement methodology known as the hybrid multi-criteria decision-making (MCDM)—failure mode and effects analysis (FMEA) approach. This approach is based on prioritizing crucial factors for any failure modes in the industrial setting. The presented case study demonstrates the application of a hybrid approach for identifying the fundamental causes of industrial pressure relief valve failure modes and malfunctions. This investigation highlights the applicability of FMEA as a methodology for determining causes and executing remedial actions to keep failures from happening again. FMEA helps uncover the underlying causes of industrial pressure relief valve failures, while the integration of the hybrid MCDM methodology enables the application of four integrated MCDM methods to identify crucial factors. The adopted model addresses the shortcomings of the conventional FMEA by accurately analyzing the relationships between the risk factors and by utilizing several MCDM methods to rank failure modes. Following the application of the adopted methodology, it was discovered that the high-risk failure modes for the pressure relief valve included misalignment of wire, normal wear/aging, rejection of machined parts, mismatch of mating parts, and corrosion. Therefore, risk managers should prioritize developing improvement strategies for these five failure modes. Similarly, failures comprising debris, delayed valve opening, internal leakage, premature valve opening, and burr foreign particles were determined as second essential groups for improvement. [ABSTRACT FROM AUTHOR]

Platform-based product development has been applied extensively in discrete manufacturing industry to accommodate changing market demands. Nevertheless, while process industry manufacturers face similar market demands, the topic is only sparsely covered in literature. Through a systematic review of the literature, this study uncovers the definitions used, drivers behind, approaches and methods applied, and industry examples of platform-based product development in the process industry. Based on these analyses, a research agenda is then proposed to further the knowledge of this topic. The study identified existing definitions of key platform-related terms used in several studies and furthermore discovered new definitions for some terms. The most prominent drivers behind pursuing platform-based product development was found to be cost reduction and productivity of product development, with development lead time reduction playing a less significant role. Literature related to platform-based product development focuses primarily on product design and development issues, with less attention given to market, manufacturing, and supply chain issues. Only few industrial cases were identified within the process industry while multiple anecdotal descriptions were discovered. For future research, further insight into key platform concepts, applicability of existing methods, broader value chain focus and detailed industrial cases are considered relevant. [ABSTRACT FROM AUTHOR]

Site-specific factors are expected to influence the indication of cost-optimal decarbonization technology for the carbon-intensive process industry. This work presents a framework methodology to enhance the comparative analysis of decarbonization alternatives using site-specific techno-economic analysis, incorporating pertinent site-specific factors to obtain an enhanced indication of the optimal decarbonization solution. Site-specific cost factors such as energy supply options, space availability, site-layout constraints, local CO2 interconnections, forced downtime, and premature decommissioning are considered. Qualitative site-specific factors and technology-specific attributes are assessed via expert elicitation with a retrofitability assessment matrix, generalizable to other process industries considering their site-level conditions. The framework methodology is demonstrated with a steam cracker plant case study, considering post-combustion CO2 capture and pre-combustion CO2 capture with hydrogen-firing in the cracker furnaces as decarbonization options. Results complemented with factor-specific sensitivity analysis highlight the extent of cost-escalation due to site-specific factors. The primary cost-contributing factor to retrofitability was the impact on production in existing sites, followed by the opportunity cost of utilizing valuable space on-site. Finally, pre-combustion CO2 capture was found to be the optimal solution, offering significant site-specific advantages, with the lowest CO2 avoidance cost and reduced overall risk over the residual lifetime of the host plant.

Abstract Process industry suffers from production management in terms of efficiency promotion and waste reduction in large scale manufacturing due to poor organization of the intricate relational databases. In order to enhance the suitability of intelligent manufacturing systems in process industry, this study proposed an innovative top-down structure Knowledge Graph (KG) for process fault diagnosis, and papermaking was taken as a case study. The KG consists of a normalized seven-step-built ontology, which extracted instances of papermaking knowledge via Protégé software. The exported OWL file was imported into Neo4j software for visualization of the KG. The application in papermaking drying process for fault diagnosis shows that it can depict the material and energy flows throughout the process with a clearer relationship visualization than traditional measures. They also enable rationale search for faults and identification of their potential causes. The built KG efficiently manages the vast knowledge of the process, stores unstructured data, and promotes the intelligent development of process with high reusability and dynamicity that can rapidly import new production knowledge as well as flexibly self-updating.

Process industry suffers from production management in terms of efficiency promotion and waste reduction in large scale manufacturing due to poor organization of the intricate relational databases. In order to enhance the suitability of intelligent manufacturing systems in process industry, this study proposed an innovative top-down structure Knowledge Graph (KG) for process fault diagnosis, and papermaking was taken as a case study. The KG consists of a normalized seven-step-built ontology, which extracted instances of papermaking knowledge via Protégé software. The exported OWL file was imported into Neo4j software for visualization of the KG. The application in papermaking drying process for fault diagnosis shows that it can depict the material and energy flows throughout the process with a clearer relationship visualization than traditional measures. They also enable rationale search for faults and identification of their potential causes. The built KG efficiently manages the vast knowledge of the process, stores unstructured data, and promotes the intelligent development of process with high reusability and dynamicity that can rapidly import new production knowledge as well as flexibly self-updating. [ABSTRACT FROM AUTHOR]

Dense medium separation is one of the most widely used methods for coal beneficiation, and an accurate estimation of product coal ash is the prerequisites to maximize the resource value. In this work, data pre-cleaning and multi-layer feature learning process of deep learning are explored to predict the product quality more accurately. The research results show that, the deep learning model considering the time series characteristics of process variables can predict the change of product quality better, which could be attributed to the fact that the full considers time series relationship between process data and product quality. Specifically, the prediction value obtained by using a single-layer, multi-unit recurrent neural network model (GRU) is improved with Root Mean Square Error (RMSE) at 0.061, and the RMSE for multi-step prediction (10 min) is 0.262. What's more, the prediction error gradually decreases and tends to be smooth with increasing time step. It is suggested that the time step used for data reconstruction should be the time of raw coal coming through to the clean coal belt. This work provides an innovative idea for improving online prediction of coal ash and is of significant importance for precise control of dense medium separation in application. [ABSTRACT FROM AUTHOR]

Accidents in process industry occur frequently with serious casualties and property losses. This paper builds an accident causation model of process industry based on system thinking by dividing the accident causation system into 4 subsystems and 22 factors. A combination of grey relational analysis and correspondence analysis is conducted to carry out a structured analysis of the collected data. The research contains three main parts: (1) Grey relational analysis is used to obtain the significance ranking of 22 cause factors in process industry, and three critical cause factors are identified as "Security inspection," "Risk identification," and "Security awareness." (2) Through correspondence analysis, the correlations between three sets of variables are analyzed and the cause factors requiring focused attention are identified as "Electric spark," "Temperature," "Raw material control," "Punching phenomenon," "Equipment clogging," and "Combustible gases." (3) An intelligent monitoring scheme is developed for the critical factors of each subsystem, which aims to achieve real‐time monitoring and early warning by means of video surveillance and sensor placement for the human, equipment, and environment subsystems. The conclusions obtained from this study can be used to enhance the efficiency of safety management and reduce the probability of accident occurrence in the process industry. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work presents a sustainability assessment approach to evaluate technological innovations in secondary steel production making use of Electric Arc Furnace (EAF) technology. The assessment covers the environmental, social, and economic dimensions of sustainability by combining different tools (Life Cycle Assessment and Analytic Hierarchy Process), and also provides an integrated assessment of the overall sustainability. The approach, which can also be used to support decision-making, has been applied to a real case study of a steel plant located in Northern Italy. In the case study, environmental sustainability is positively impacted mainly by increased metal yield and reduced furnace energy consumption. The greatest social sustainability benefits are mainly related to improved ergonomic and safety conditions for workers (reduced demand for physical effort, manual handling and repetitiveness, and lower risk of accidents), as a consequence of the introduction of Industry 4.0 technologies. Regarding economic sustainability, a positive impact related to reduced cycle time, increased metal yield and quality yield, reduced maintenance and quality control costs was observed. The integrated assessment of the overall sustainability has proven to be a viable approach to manage trade-offs between the different dimensions of sustainability.

Industrial pressure relief valves must function reliably and effectively to protect pressurized systems from excessive pressure conditions. These valves are essential safety devices that act as cushions to protect piping systems, equipment, and vessels from the risks of high pressure, which can cause damage or even explosions. The objectives of this study were to minimize valve failures, decrease the number of rejected valves in the production line, and enhance the overall quality of pressure relief valves. This work introduces an integrated quality improvement methodology known as the hybrid multi-criteria decision-making (MCDM)—failure mode and effects analysis (FMEA) approach. This approach is based on prioritizing crucial factors for any failure modes in the industrial setting. The presented case study demonstrates the application of a hybrid approach for identifying the fundamental causes of industrial pressure relief valve failure modes and malfunctions. This investigation highlights the applicability of FMEA as a methodology for determining causes and executing remedial actions to keep failures from happening again. FMEA helps uncover the underlying causes of industrial pressure relief valve failures, while the integration of the hybrid MCDM methodology enables the application of four integrated MCDM methods to identify crucial factors. The adopted model addresses the shortcomings of the conventional FMEA by accurately analyzing the relationships between the risk factors and by utilizing several MCDM methods to rank failure modes. Following the application of the adopted methodology, it was discovered that the high-risk failure modes for the pressure relief valve included misalignment of wire, normal wear/aging, rejection of machined parts, mismatch of mating parts, and corrosion. Therefore, risk managers should prioritize developing improvement strategies for these five failure modes. Similarly, failures comprising debris, delayed valve opening, internal leakage, premature valve opening, and burr foreign particles were determined as second essential groups for improvement.

The most recent sustainability policies of each region of the world conjointly define that economic activities shall follow the principles of natural resource use minimisation, as well as eco-efficiency and circular economy promotion, in addition to the specific objectives defined in each policy. Most recently, a group of researchers has proposed innovative conceptual systems designated Water and Energy Integration Systems (WEIS) for issues related to water and energy use (two prominent categories of natural resources). These are based on engineering projects encompassing a multitude of processes and technologies. In this work, an assessment based on the determination of several sustainability and strategic-aims-related indicators is performed for two WEIS case studies set in the Portuguese process industry (in this case, a ceramic plant). Such an assessment serves as an expansion of previously performed studies on the economic and environmental viability associated with the installation of this type of system with the ultimate goal of proving the effective compliance of water- and energy-use-reduction-related results with sustainability and strategic aims (namely, the ones associated with the most recent policies and aspects associated with the social, economic, and environmental pillars of sustainability). The results for the overall assessment proved that the conceptualised WEIS are robust in terms of eco-efficiency, circular economy potential, and strategic objective achievement potential (with a 6.46% and 4.00% improvement for the aggregated eco-efficiency indicator having been obtained for, respectively, case studies 1 and 2, a null water discharge for both case studies, and a level of 8.58% and 6.69% of recirculated heat over total energy consumption, respectively). The obtained results prove the sustainability promotion effectiveness of the WEIS as conceptual systems. The overall set of indicators defined in this work are part of a methodology that may be used and adapted for further studies considering the innovative WEIS approach, with the specific results obtained in this work presented with the aim of their being used for comparison. [ABSTRACT FROM AUTHOR]

In the new paradigm of 'transformative' or 'mission-oriented' innovation policy, which addresses broad societal challenges, policy makers are given a large responsibility for setting or shaping the direction of socio-technical transitions. However, the literature has so far not provided much concrete advice on how to achieve directionality in practice. The main argument of this conceptual article is that a more detailed approach is needed to better understand the challenges policy makers might face when they attempt to translate societal goals into more concrete and actionable policy agendas. It identifies and discusses eight analytically derived directionality challenges: handling goal conflicts, defining system boundaries, identifying realistic pathways, formulating strategies, realising destabilisation, mobilising relevant policy domains, identifying target groups, and accessing intervention points. To illustrate these challenges, the article uses examples from the implementation of the Swedish climate goal in the process industry. [ABSTRACT FROM AUTHOR]

The production systems in process industries are confirmed to be tremendously energy-consuming, and the trust in promoting their energy efficiency has become a concern, with its precondition being to evaluate the real-time energy consumption. A widespread evaluation method is to develop a global model that employs energy audit techniques, whereas they are always carried out with few appreciations of multiple energy consumption patterns, and the utilization of energy consumption auxiliary information. To address the challenge, a two-stage clustering-based-energy consumption evaluation method is proposed for process industries in this study. Specifically, a novel structure of the fuzzy clustering method is designed with a mixture of unsupervised and semi-supervised learning stages that leverages the weighted information to independently address energy consumption patterns. Then energy consumption predictions are estimated for potential energy-optimized control. The key performance indicators of energy consumption are calculated for each pattern, and the final evaluation grade will be achieved through the fuzzy synthetic evaluation method. According to the experiment results, the proposed method delivers better evaluation results against baselines with more accurate clustering; it may provide a new thought for energy consumption evaluation and is confirmed to enable practitioners to acquire the potential benefits in engineering. [ABSTRACT FROM AUTHOR]

The need for sustainable production, efficient use of resources, energy efficiency and reduction in CO2 emission are currently the main drivers that are transforming the European process industry besides Industry 4.0. Since the potential of industrial symbiosis (IS) and energy efficiency (EE) about environmental, economic and social issues has been discovered, the interest in them is gradually increasing. The funding and investments for IS and EE are highly encouraged by the European Commission, while more and more policies as well as research and innovation (R&I) activities are initiated to promote European industry's advancement towards a circular economy and CO2 neutrality. The aim is to maintain the competitiveness and economic progress of the industry. The key to build a competitive and sustainable European manufacturing industry is to create a competent, highly qualified workforce that is capable of handling the new business models coming with IS and EE requirements and digital technologies. We can generate this by identifying the skills needs and upskilling and reskilling the current workforce accordingly by delivering the suitable training programmes. Therefore, this work identifies the most critical skills needs related to IS and EE for six different energy-intensive sectors (steel, ceramic, water, cement, chemical and minerals) in Europe. The effect of the digital transformation on the skills needs is as well discussed. The identified skills are aimed to be included in vocational education and training (VET), tertiary education and other kinds of training curricula. We also identify the cross-sectoral most representative job profiles linked with EE and IS in these sectors and demonstrate the methodology for the selection process. Furthermore, we present a key tool for identifying the most significant current and future skills requirements. Also, we define the critical skill gaps of the European process industry using this tool. Once the skill gaps are defined, they can be reduced by delivering well-developed continuous trainings. We also link our work to the respectable ESCO, the European Classification of skills, competences, qualifications and occupations, to attain a common ground with other studies and frameworks, minimise the complexity and contribute to their work. Our work is developed to be an academic and industrial guideline to prepare well-developed training programmes to deliver the needed skills. [ABSTRACT FROM AUTHOR]

Digitalization shows that data and its exchange are indispensable for a versatile and sustainable process industry. There must be a shift from a document‐oriented to a data‐oriented process industry. Standards for the harmonization of data structures play an essential role in this change. In engineering, DEXPI (Data Exchange in the Process Industry) is already a well‐developed, machine‐readable data standard for describing piping and instrumentation diagrams (P&ID). In this publication, industry, software vendors, and research institutions have joined forces to demonstrate the current developments and potentials of machine‐readable P&IDs in the DEXPI format combined with artificial intelligence. The aim is to use graph neural networks to learn patterns in machine‐readable P&ID data, which results in the efficient engineering and development of new P&IDs. [ABSTRACT FROM AUTHOR]

Summarizing a key use case of a research workstream of the German publicly funded KEEN project, methods and tool chains are demonstrated to extract and to contextualize process data in an automated way based on engineering information. The contextualized process data serves as a high‐quality data source for machine learning methods. The article covers the applied basic methodical approaches, design decisions and the results of a successful pilot installation of the developed tool chain. [ABSTRACT FROM AUTHOR]

Data collection in process industry yields a variety of data types. To maintain the knowledge about the data and to enable finding and reusing them (FAIR data), a common description with metadata is necessary. In the project KEEN, ProMetaS – the Process Engineering/Industry Metadata Schema – was developed. It defines various metadata categories adhering to available metadata standards. In this article, ProMetaS, its implementation into the KEEN data platform, and its application in two use cases is described. [ABSTRACT FROM AUTHOR]

Asphalt mixes comprise aggregates, additives and bitumen. The aggregates are of varying sizes, and the finest category, referred to as sands, encompasses the so-called filler particles present in the mixture, which are smaller than 0.063 mm. As part of the H2020 CAPRI project, the authors present a prototype for measuring filler flow, through vibration analysis. The vibrations are generated by the filler particles crashing to a slim steel bar capable of withstanding the challenging conditions of temperature and pressure within the aspiration pipe of an industrial baghouse. This paper presents a prototype developed to address the need for quantifying the amount of filler in cold aggregates, considering the unavailability of commercially viable sensors suitable for the conditions encountered during asphalt mix production. In laboratory settings, the prototype simulates the aspiration process of a baghouse in an asphalt plant, accurately reproducing particle concentration and mass flow conditions. The experiments performed demonstrate that an accelerometer positioned outside the pipe can replicate the filler flow within the pipe, even when the filler aspiration conditions differ. The obtained results enable extrapolation from the laboratory model to a real-world baghouse model, making it applicable to various aspiration processes, particularly those involving baghouses. Moreover, this paper provides open access to all the data and results used, as part of our commitment to the CAPRI project, with the principles of open science. [ABSTRACT FROM AUTHOR]

In the era of digitalization, the process industry is one of the sectors most affected by the need for change. The adoption of IoT-based intelligent monitoring systems for the collection of real-time measurements of energy and other essential operational variables, on one hand, makes it possible to accumulate big data useful for the company management to monitor the stability of the production process over time, and on the other hand, helps to develop predictive models that enable more efficient work and production. The OTTORTO project stems from the need of the FARRIS company to adapt its production line to agriculture 4.0 policies, responding to the higher goals of digitization and technological transition imposed at the national and EU level. The objectives of the current study are (i) to present an "ad hoc" customized intelligent and multi-parameter monitoring system to derive real-time temperature and humidity measurements inside the company's industrial drying kilns; and (ii) to show how it is possible to extract information from operational data and convert it into a decision support too and an effective knowledge medium to better understand the production process. Studying the correlations between temperature and humidity measurements showed that for most of the observation period, the system was thermodynamically quite stable in terms of major operational risks, such as humidity saturation inside the kilns causing condensation on the products to be dried. However, to remedy the occasional occurrence of such inefficiencies, implementing kilns with the introduction of forced air extraction systems could bring significant benefits in terms of improved energy-environmental performance. [ABSTRACT FROM AUTHOR]