A Mathcad‐based educational experience to address the design of nonisothermal plug flow reactors

Mathcad is a simple-to-use and intuitive mathematical software that helps students to minimize the mathematical difficulties involved in solving engineering problems. The design of nonisothermal plug flow reactors (PFR) is a fundamental issue within the field of chemical reaction engineering; however, its teaching–learning process is hindered by students' mathematical difficulties in solving ordinary differential equations. In this paper, the software Mathcad was conveniently integrated into an educational experience through the resolution of two real case studies. In the first one, a simple liquid-phase reaction is considered in a PFR working at different operating conditions, whereas the second case evaluates a PFR taking place multiple reactions (parallel reactions) with a heat exchanger attached. The assessment of this experience, which was held into two 5-h Mathcad workshops, revealed that Mathcad made the design of non-isothermal PFR more appealing, facilitated the understanding of the design process, and brought another dimension to the way the students perform complex calculations

Novel methods for modelling, design and control of advanced well completion performance

This thesis presents new approaches to modelling of reservoir and well flow performance when the wells are completed with Advanced Well Completions (AWC). The particular focus of this research is modelling fluid flow in the reservoir-AWC-well systems using simple, reduced-physics models that do not necessarily require detailed reservoir description yet are comprehensive enough to capture the major trends in the system to achieve the AWC performance evaluation and design objectives. This allows rapid screening and design of the AWC technology that is at the same time less subject to the reservoir uncertainty due to less input on the reservoir geology required. Such models can also complement, e.g. in order to steer or speed-up, the existing AWC modelling and design workflows that involve full reservoir simulation. The outcome aids reliable investigation of expected AWC and reservoir performance derived from the available data in order to perform quick scoping of reservoir management concepts and options prior, or in addition, to detailed modelling. This is particularly important in real field models where the numerical reservoir simulation is often uncertain and computationally expensive, especially when coupled with AWC wellbore models. The study first introduces three main classes of flow control technologies used in AWCs: passive (realised with Inflow Control Devices - ICDs), the recently introduced autonomous (Autonomous Flow Control Devices - AFCDs) and active (Inflow Control Valves - ICVs). The traditional workflows for AWC performance modelling and design using commercial numerical reservoir simulation for each AWC class are discussed and evaluated. Finally, the novel, rapid AWC modelling methods are developed that can reliably inform reservoir development and management decisions. The thesis develops the following approaches and modelling methods aimed at analysis and design of AWC flow performance: 1. The model describing the trade-off between the well productivity loss and the improved inflow equalisation in AWCs in well with heel-toe effect and heterogeneous reservoir 2. The technique to estimate the additional, long-term value derived by controlling zonal flow rate (AWC’s well) in pistonlike and non-pistonlike displacement. 3. The concept relating the various short-term, AWC design methods and their long term outcomes. 4. Characterisation of inter-well and inter-layer connectivity for waterflooded reservoirs developed with wells completed with zonal gauges and ICV completions. 5. Consequently, the framework for optimal ICV control when the inter-well connectivities are estimated. This work enables application of rapid AWC design and optimisation. Moreover, integration with the reservoir waterflood monitoring results in a better understanding of the reservoir performance. Practical utility of the proposed methods is illustrated in case studie

Exploring the Synergy between Industry 4.0, Quality 4.0, and Lean Production for Improved Quality in Manufacturing

The manufacturing industry has experienced significant changes with the advent of technological advancements. Industry 4.0 and Quality 4.0 have emerged as new paradigms focusing on the use of digital technologies to enhance efficiency and quality in manufacturing. Additionally, the long-established lean approach aims to minimize waste and improve overall quality. Integrating these approaches with Industry 4.0 and Quality 4.0 has the potential to transform the industry by optimizing quality, efficiency, and profitability. Therefore, this study aims to explore the interconnections between lean, Industry 4.0, and Quality 4.0, and their impact on quality in manufacturing. The research begins by providing the necessary background, starting with a comprehensive examination of the concept of quality. Subsequently, the study presents an overview of lean principles, followed by an analysis of the concepts of Industry 4.0 and Quality 4.0. The theoretical framework also explores the relationship between these concepts. Following the theoretical analysis, the empirical part of the study was conducted using a survey questionnaire. The survey was distributed to 190 medium-sized manufacturing companies in Finland, ultimately receiving responses from 44 organizations. This survey aimed to gather practical insights into the implementation of lean, Industry 4.0, and Quality 4.0 practices within the manufacturing context. Based on the findings derived from the survey data, lean tools have a broader adoption compared to Industry 4.0 and Quality 4.0 practices. Moreover, implementing these tools and practices has shown a positive impact on company performance, sparking a growing interest among companies to explore and implement additional tools. The survey also highlights a strong inclination among organizations to embrace Industry 4.0 and Quality 4.0 practices, showcasing their increasing recognition of the potential benefits and transformative capabilities of these innovative methodologies in driving organizational success

Mass Transfer

This book covers a wide variety of topics related to advancements in different stages of mass transfer modelling processes. Its purpose is to create a platform for the exchange of recent observations, experiences, and achievements. It is recommended for those in the chemical, biotechnological, pharmaceutical, and nanotechnology industries as well as for students of natural sciences, technical, environmental and employees in companies which manufacture machines for the above-mentioned industries. This work can also be a useful source for researchers and engineers dealing with mass transfer and related issues

Development of a modeling algorithm to predict lean implementation success

”Lean has become a common term and goal in organizations throughout the world. The approach of eliminating waste and continuous improvement may seem simple on the surface but can be more complex when it comes to implementation. Some firms implement lean with great success, getting complete organizational buy-in and realizing the efficiencies foundational to lean. Other organizations struggle to implement lean. Never able to get the buy-in or traction needed to really institute the sort of cultural change that is often needed to implement change. It would be beneficial to have a tool that organizations could use to assess their ability to implement lean, the degree to which they have implemented lean, and what specific areas they should focus on to improve their readiness or implementation level. This research investigates and proposes two methods for assessing lean implementation. The first is utilizing standard statistical regression. A regression model was developed that can be used to assess the implementation of lean within an organization. The second method is based in artificial intelligence. It utilizes an unsupervised learning algorithm to develop a training set corresponding to low, medium, and high implementation. This training set could then be used along with a supervised learning algorithm to dynamically monitor an organizations readiness or implementation level and make recommendations on areas to focus on to improve implementation success”--Abstract, page iv

The Relationship between Knowledge Management Tools and Interprofessional Healthcare Team Decision Making

Rising costs and continued risks in patient care indicate that knowledge management (KM) tools have not been fully recognized in healthcare. A case study was conducted to determine how KM tools might support the decision-making process of interprofessional teams. The study was predominately qualitative with a quantitative supplemental component. A questionnaire was used to collect data; this questionnaire contained open-ended questions along with Baggs\u27 Collaboration and Satisfaction about Care Decisions and Anderson & West\u27s Team Climate Inventory instruments. Responses to open-ended questions were reviewed, categorized, and coded as part of the qualitative analysis. Descriptive statistics were completed from Likert scale responses. Participants were selected from existing interprofessional transitional care teams in clinics at a VA hospital; a total of 29 participants volunteered. The framework of decision making and KM was the basis for the study. The research concentrated on interprofessional teams\u27 environment characteristics of trust, collaboration, and sharing. The intended goal of the study was to understand how satisfaction in the delivery of collaborative care decisions and the team climate might influence the success of using or implementing KM tools. Key findings included the importance of communication to support teams\u27 knowledge sharing and collaboration; findings also revealed how the satisfaction in the patient care decision-making process may influence a team\u27s climate for innovation, collaboration, and sharing. These insights may inform the development and implementation of healthcare KM tools. Through the use of KM tools to support clinical decision making, opportunities become available to improve patient care and reduce costs, which lead to a positive social change in minimizing the disparity in the healthcare delivery system

Microcosm Evaluation of Natural and Biologically-enhanced Abiotic Transformation of Chlorinated Ethenes in Low Permeability Formations

Trichloroethene (TCE) is one of the most commonly found hazardous compounds at Superfund sites, especially in groundwater. Its volatility and toxicity pose a threat to drinking water safety and human health. In the past decades, research on the fate of TCE in the environment has shifted to contamination of low-permeability formations, for example fractured bedrock aquifers. TCE back-diffusion from low permeability zones and management of persistent TCE groundwater plumes caused by this long-term source zone pose major challenges for remediation. Monitored natural attenuation (MNA) is a cost-effective remediation strategy that has gradually gained acceptance by regulators. Compared to extensive active remediation of persistent TCE plumes, MNA is more attractive for its cost-effectiveness. However, acceptance of MNA as a remediation strategy depends on adequate documentation of in situ TCE degradation at meaningful rates. Collecting lines of evidence in support of TCE natural degradation is an essential component of MNA. In this study, two lines of evidence were collected for three Department of Defense sites overlain with fractured bedrock that are experiencing TCE back-diffusion. The first type of evidence involved monitoring groundwater for dissolved gas products associated with abiotic degradation (i.e., acetylene, ethene, and ethane) using a novel passive vapor diffusion (PVD) sampler. The other line of evidence was based on the use of intact rock core microcosms to estimate TCE degradation rate constants. This is the first study to employ the use of 14C-labeled TCE in intact rock core microcosms. Estimating the TCE degradation rate constants was accomplished with numerical modeling