Techno-economic feasibility assessment of sorption enhanced gasification of municipal solid waste for hydrogen production

Waste-to-fuel coupled with carbon capture and storage is forecasted to be an effective way to mitigate the greenhouse gas emissions, reduce the waste sent to landfill and, simultaneously, reduce the dependence of fossil fuels. This study evaluated the techno-economic feasibility of sorption enhanced gasification, which involves in-situ CO₂ capture, and benchmarked it with the conventional steam gasification of municipal solid waste for H₂ production. The impact of a gate fee and tax levied on the fossil CO₂ emissions in economic feasibility was assessed. The results showed that the hydrogen production was enhanced in sorption enhanced gasification, that achieved an optimum H₂ production efficiency of 48.7% (T = 650 °C and SBR = 1.8). This was 1.0% points higher than that of the conventional steam gasification (T = 900 °C and SBR = 1.2). However, the total efficiency, which accounts for H₂ production and net power output, for sorption enhanced gasification was estimated to be 49.3% (T = 650 °C and SBR = 1.8). This was 4.4% points lower than the figure estimated for the conventional gasification (T = 900 °C and SBR = 1.2). The economic performance assessment showed that the sorption enhanced gasification will result in a significantly higher levelised cost of hydrogen (5.0 €/kg) compared to that estimated for conventional steam gasification (2.7 €/kg). The levelised cost of hydrogen can be reduced to 4.5 €/kg on an introduction of the gate fee of 40.0 €/t_MSW. The cost of CO₂ avoided was estimated to be 114.9 €/tCO₂ (no gate fee and tax levied). However, this value can be reduced to 90.1 €/tCO₂ with the introduction of an emission allowance price of 39.6 €/tCO₂. Despite better environmental performance, the capital cost of sorption enhanced gasification needs to be reduced for this technology to become competitive with mature gasification technologies.     

互联网时代下的学校图书管理信息化建设

如何在互联网时代打造一个信息化的管理模式,已经成为广大学校图书管理发展过程中亟待处理的问题。文章就互联网时代下的高职图书管理信息化建设进行了详细探讨,以期能够给广大同仁提供一些借鉴参考,共同为图书管理工作的现代化改革和发展贡献力量。     

Investigation of liquid water heterogeneities in large area proton exchange membrane fuel cells using a Darcy two-phase flow model in a multiphysics code

Proper management of the liquid water and heat produced in proton exchange membrane (PEM) fuel cells remains crucial to increase both its performance and durability. In this study, a two-phase flow and multicomponent model, called two-fluid model, is developed in the commercial COMSOL Multiphysics® software to investigate the liquid water heterogeneities in large area PEM fuel cells, considering the real flow fields in the bipolar plate. A macroscopic pseudo-3D multi-layers approach has been chosen and generalized Darcy's relation is used both in the membrane-electrode assembly (MEA) and in the channel. The model considers two-phase flow and gas convection and diffusion coupled with electrochemistry and water transport through the membrane. The numerical results are compared to one-fluid model results and liquid water measurements obtained by neutron imaging for several operating conditions. Finally, according to the good agreement between the two-fluid and experimentation results, the numerical water distribution is examined in each component of the cell, exhibiting very heterogeneous water thickness over the cell surface.     

Development of high pressure membraneless alkaline electrolyzer

A technology for cyclic generation of hydrogen and oxygen using electrodes made of variable valency material that does not need the use of separating ion-exchange membranes is presented. The technological solution enables to fabricate electrolyzers for uninterrupted producing high-pressure hydrogen with reduced energy intensity of the production. The total work for compressing 1 m³ of hydrogen and 0.5 m³ of oxygen has been estimated. Results of investigation of influence of discrete supply of DC current to the electrolysis cell, in order to improve the processes of gas evolution and to simplify the power systems of the electrolysis plant, have been considered. There is also considered an electrolysis installation equipped with a thermosorption compressor in which LaNi₅ is used as a hydride-forming compound. The comparative characteristics of the developed electrolyzer and the currently used hydrogen generators are given.     

Analysis of operating limits and combustion state regulation for low-calorific value gases in industrial burners

The effective and efficient utilization of low-calorific value (LCV) gases has gained increasing attention in scientific research and industrial fields. In this study, the combustion characteristics of three LCV gases in practical devices are analyzed by using a nonadiabatic perfectly stirred reactor model. The complete steady-state solution in the temperature-residence time parameter space is obtained with arc-length continuation. The stable operation region is quantified by the eigenvalue analysis. The transition of solution curves is quantified with heat loss coefficient. Five key system parameters are systematically investigated on their effects on stability limits. With the combustion performance being quantified by a combustion state index, a combustion state regulation method is proposed to find the optimal regulation path of system parameters. Active subspace method is further applied to shorten the regulation step by identifying the active direction. The proposed method and findings are useful for optimal regulation of burning LCV gases in industrial burners.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

A practical development of engineering simulation-assisted educational AR environments

Engineering simulations have opened several gates for today’s chemical engineers. They are powerful tools to provide technical content as physics-based numerical solvers. Augmented reality (AR) and virtual reality (VR), on the other hand, are already underway to digitize environments in many fields. The combination of AR/VR environments and simulations in engineering education has been attracting widespread interest. Literature has demonstrated a massive amount of digital educational environments in several contexts as being complementary to conventional educational methods. Nevertheless, hosting technical content produced by engineering simulations with educational AR/VR is still challenging and requires expertise from multiple disciplines throughout the technical development. Present work provides a facile and agile methodology for low-cost hardware but content-wise rich AR software development. Inspired by the Covid-19 pandemic, a case study is developed to teach chemical-engineering concepts using a liquid-soap synthesis process. Accordingly, we assess and conclude the digital development process to guide inexperienced developers for the digitalization of teaching content. The present contribution serves as an example of the power of integrating AR/VR with traditional engineering simulations for educational purposes. The digital tool developed in this work is shared in the online version.     

Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney,Melbourne and Brisbane

In recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.     

Use of a smartphone-based Student Response System in large active-learning Chemical Engineering Thermodynamics classrooms

Smartphones are a promising tool as student response systems (SRS) for interactive teaching due to their widespread diffusion. Here, the main purpose is to assess the efficacy of smartphone-based SRS in large classroom settings of undergraduate Thermodynamics, as representative of engineering courses requiring high-level cognitive skills for problem solving. Four sets of multiple-choice questions were presented during the course. Overall, the results refer to 1055 students between control and SRS classes, each corresponding to a3 years period.One of the main results of this work is the strong linear correlation between the average questionnaire score and the final exam grade (R² = 0.91). A similar correlation, although with a lower value of R², is already found in the first questionnaire, thus showing the SRS high predictive power of class performance. The results of this study provide guidance for a quantitative use of smartphone-based SRS in teaching basic disciplines. The SRS monitoring capability allows early detection of struggling students, thus paving the way to personalized tutoring and improved student engagement in active learning practices. This approach is especially important in emergency situations, such as the SARS-Cov-2 pandemic, when distance learning is widely adopted, and remote interactive tools are highly needed.     

Industry 4.0: a supply chain innovation perspective

The Fourth Industrial Revolution – also known as Industry 4.0 (i4.0) – comprises the digitalisation of the industrial sector. This paper uses the theoretical lens of supply chain innovation (SCI) to investigate the implications of i4.0 on supply chain management. For these purposes, the method of structured content analysis is applied to more than 200 use cases of i4.0-enabled SCI introduced by both established and startup companies. i4.0-enabled SCI manifests along three dimensions: process, technology, and business architecture. The key findings of this study can be summarised as follows: first, i4.0-enabled SCI extends the initial focus on productivity improvements in SC processes towards scalability and flexibility. Second, extant i4.0 solutions rely mostly on analytics and smart things while omitting smart people technology and the human-centric approach associated with the i4.0 paradigm. Third, established companies adopt i4.0 merely to sustain their existing business architectures while startup companies radically change their operating models, relying heavily on data analytics and the platform economy. Consequently, established companies pursue a problem-driven, engineering-based approach to SCI while startup companies follow an ‘asset-light’, business-driven approach. Lastly, there are two distinct approaches to digitalising operational SC processes: platform-based crowdsourcing of standard processes and on-demand provision of customised services.