Development Engineering最新文献

Selecting the appropriate technology for providing electricity to rural communities depends upon evaluating the cost of a potential installation. For some rural communities, locally manufactured technology, in the form of wind and hydropower, can be effective. However, often the cost of these locally manufactured technologies is largely unknown. Access to costing data allows the economic viability of a site to be compared with other options. Furthermore, it enables benchmarking, allowing the expected total cost of an installation, or individual sub-systems, to be compared with quotations. This paper attempts to address the current lack of publicly available costing information for locally manufactured micro-hydropower equipment. A methodology is presented where quotations are provided by micro-hydropower manufacturing companies in Nepal for randomly generated sites. Using that information, they provided a quotation for various sub-systems. This data allows comparison of the cost of major components and the influence of turbine type. Through a linear regression model, expression have been developed that can be used to determine the expected cost for both Pelton and Crossflow turbine installations. The accuracy of these expressions is compared with previous costing models, the outcomes of the work and their significance in the context of Nepal and elsewhere is discussed. The key contribution of this work is establishing numerical expressions which allow proposed costs of micro-hydropower equipment to be rapidly evaluated.

High-performance wastewater treatment technologies suited to the urban environment remain largely inaccessible to developing countries due to financial constraints. Instead, inadequate technologies are being used that adversely affect the quality of water resources and limit their sustainability. One high performing technology that offers possible solution is a packaged version of the integrated fixed-film activated sludge (IFAS) system consisting of a 20 m³ aerobic reactor and a 4.2 m³ settlement tank. The present work has investigated aspects of this typically-expensive solution that can be economized to improve its uptake in these countries. To achieve this a life cycle cost analysis (LCCA) was performed and potential savings identified. The results obtained show that the life cycle cost is $0.31/m³ and that costs primarily occurred at the construction stage (11.9%) and the operation and maintenance stage (88.1%) with negligible disposal costs. A reduction of up to 42.4% in construction costs were shown to be accessible by adopting other materials such as high-density polyethylene (HDPE) or to a lesser extent glass-fibre reinforced polymer (GFRP). The greatest single cost in the life cycle was found to be incurred by aeration (48.9%), requiring expenditure of $0.15/m³, however the use of intermittent aeration (IA) could reduce this further to $0.08/m³. Further work is suggested to investigate the broader sustainability of the different aeration strategies in light of these economic results.

The rise in sensor-based monitoring in the cookstove sector has been driven by the need for objective quantitative performance evaluation within context of use, and is especially useful if monitoring activities can be conducted by in-country project staff. This research explores the insights achievable from single and cross-sensor analysis following simultaneous in-home deployment of stove temperature loggers, weight-based fuel use loggers, and indoor PM concentration loggers deployed with remote guidance by researchers. Longitudinal performance metrics of an improved metal biomass stove with a chimney within its context of use were obtained using sensor suites consisting of stove temperature sensors (EXACT), household air pollution sensors (HAPEx), and fuel use sensors (FUEL) deployed in 48 households in the Taplejung and Panchthar districts of eastern Nepal. Households in the Taplejung district, comprised mostly of commercial tea houses, had a median reduction in daily household average PM concentration of 45.7% (n = 17) and a median reduction in logged household fuel use of 24.5%, or 2.17 kg/day (n = 15). Households in the Panchthar district comprised of smaller households had a median reduction in daily household average PM concentration of 64.5% (n = 19) and a median reduction in logged household fuel use of 8.13%, or 0.42 kg/day (n = 23). Cross-sensor analysis included use of household PM concentration to verify cooking event initiation and extraneous rises in PM outside of identified cooking events for potential exclusion. Household fuel use profiles were compared to known cooking events to determine whether a household had consistently interacted with the fuel measurement system as instructed, indicating which data were reliable and those that should be flagged. While both cross-sensor analysis and verification methods were examined as potential ways to obtain more information from the gathered data, further development of automated analytics platforms are needed before they can be used as reporting tools by project staff.

Developers and users situated in low-resource settings are faced with unique contextual and infrastructure challenges when accessing and consuming cloud-based services. In low-resource settings, access to cloud services and platforms is usually characterized by low-end computing devices and often unreliable and slow mobile broadband Internet connections. In this paper, we discuss key challenges for developing for and accessing cloud services in resource constrained settings, namely, (1) Frequent Internet partitions and bandwidth constraints, (2) Data jurisdiction restrictions, (3) Vendor lock-in, and (4) Poor quality of service. Inspired by these challenges, we propose a set of important design considerations and properties for a resilient multi-cloud service layer, that includes: (1) Containerization and orchestration of applications, (2) Application placement and replication, (3) Portability and multi-cloud migration, (4) Resilience to network partitions and bandwidth constraints, (5) Automated service discovery and load balancing, (6) Localized image registry, and (7) Support for platform monitoring and management. We present an implementation and validation case study, Crane Cloud, an open source multi-cloud service abstraction layer built on-top of Kubernetes that is designed with inherent support for resilience to network partitions, microservice orchestration (deployment, scaling and management of containerized applications), a localized image registry, support for migration of services between private and public clouds to avoid vendor lock-in issues and platform monitoring. We evaluate the performance and user experience of Crane Cloud by implementing and deploying a computational and bandwidth intensive machine learning system. The results show lower response times of the system on Crane Cloud compared with hosting on other public clouds. The Crane Cloud platform is serving as a cloud-service for students and developers in low-resource settings and also as an education platform for cloud computing.

Internet-connected sensor technologies have recently been used to monitor water service infrastructure in remote settings. In this study, 397 groundwater pumps were observed in Plateau State, Nigeria over 12 months in 2021. Two hundred of these sites were instrumented with remotely reporting electronic sensors, including 100 hand-pump sensors, 50 electrical pump sensors, and 50 cistern water-level sensors. Every two months, phone calls and site visits were used to collect a ground-truth of pump functionality: whether the pump was capable of delivering water, regardless of actual use. Our study examined: (1) What are the operating characteristics and trends of these different kinds of water pumps?; (2) Can water-point functionality be predicted with electronic sensors?; and (3) Does the instrumented water-point sample accurately represent average water-system functionality across the region? An automated classifier generated functional/non-functional diagnostics for instrumented pumps on a weekly basis. Classifier diagnostics were compared to ground-truth data, showing an overall accuracy of 91.7% (96.1% for hand-pumps, 63.9% for cisterns, and 93.2% for electrical boreholes), with high fleet-wide sensitivity in correctly identifying a functional pump (94.4%), but poor overall specificity in correctly identifying a non-functional, broken pump (25.0%). This discrepancy is attributable to the sensors’ difficulty in distinguishing between a broken pump and an unused pump. Varied patterns were seen in pump usage as a function of rainfall, with hand-pump use decreasing significantly, electrical pump usage decreasing to a lesser degree, and cistern use increasing in response to local rainfall. A comparison of the 200 instrumented to 197 non-instrumented sites showed statistically similar repair and failure rates. The high overall accuracy of the sensor–diagnostic system—and the demonstration that sensor-instrumented sample sites can represent population-level breakdown and repair frequencies—suggests this technology’s utility in supporting sample-based monitoring of overall water pump functionality and water volume delivery. However, the poor performance of the system in distinguishing between broken and unused pumps will limit its ability to trigger repair activities at individual pumps.

As of May 2021, the current COVID-19 pandemic is still plaguing the world, challenging all the countries and their health systems, globally. In this context, conditions typical of low-resource settings surfaced also in high-resource ones (e.g., the lack of essential medical equipment, of resources etc.), while exacerbating in the already resource-scarce settings, because of COVID-19. This is the case of oxygen concentrators that are one of the first-line medical devices for treating COVID-19 patients. Since the beginning of 2020, their demand has been rapidly growing worldwide, aggravating the situation for low-resource settings, where the availability of devices providing oxygen-enriched air was already scarce. In fact, due to their delicacy, the lack of spare parts and of an appropriate health technology management system, oxygen concentrators can often be found broken or not working properly in these settings. The underlying problems have deep roots. The current regulatory frameworks and standards, which are set by high-income countries, are too stringent, and do not take into account the limited resources of poorer settings. Thus, they are often inapplicable in such settings. One of the main issues affecting the oxygen concentrators, is that related to the filters, which are designed to filter out dust, particles, bacteria, and to be used in medical locations complying with international standards (e.g., the air filtration level in a surgical theatre in Italy is at 99.97%). When used in low-resource settings, which do not comply with these standards and face several challenges (e.g., dust), these filters have a much-reduced lifespan.

For these reasons, this paper aims to present the redesign of the inlet filter of an oxygen concentrator, which is used to prevent gross particles to enter the device. The redesign is based on a reverse engineering approach, and on the use of 3D-printing along with activated charcoal. After testing the filtration efficiency with a particle counter, the filter design has been refined through several iterations. The final prototype performs particularly well when filtering particles above 1 μm (with a filtration efficiency of 64.2%), and still has a satisfactory performance with any particle size over 0.3 μm (with a filtration efficiency of 38.8%). Following the United Nations Sustainable Development Goals, this project aims to empower local communities, and start a positive trend of self-sustained supply chain of simple spare parts for medical devices, leveraging on frugal engineering, 3D-printing, locally produced activated charcoal, and circular economy.

Despite decades of global development programming, poverty persists in the low-and-middle-income countries targeted by these efforts. Training approaches to global development must change and the role of engineers in these efforts must evolve to account for structural and systemic barriers to global poverty reduction. Rapid growth in Global Engineering graduate programs in the United States and Canada creates an opportunity to unify efforts between academic institutions and ensure that programs align with the sector's needs as identified by practitioners. To build consensus on how to equip engineering students with the knowledge, skills and attitudes necessary, we convened practitioners, faculty and graduate students for a two-day workshop to establish an agreed-upon Global Engineering body of knowledge. The workshop was informed by a pre-event survey of individual participants and representatives of participating academic institutions with graduate programs in Global Engineering or a related field. Through the workshop breakout sessions and post-event work by the authors, we developed the following priority learning objectives for graduate education in global engineering: Contextual Comprehension and Analysis; Cross-cultural Humility; Global Engineering Ethics; Stakeholder Analysis and Engagement; Complex Systems Analysis; Data Collection and Analysis; Data-driven Decision Making; Applied Engineering Knowledge; Project Design; Project Management; Multidisciplinary Teamwork and Leadership; Communication; Climate Change, Sustainability, and Resilience; Global Health; and Development Economics. Although technical skills are central to preparing the next generation of Global Engineers, transversal and interdisciplinary skills are equally important in equipping students to work across sectors and account for barriers to global development and equity.

We present a case study of successful uptake of a productive use of electricity (PUE) co-located at an off-grid clinic powered by OffGridBox in Rwanda. We develop a techno-economic analysis of the standardized, modular, and redeployable power supply technology, characterizing cost components, revenue considerations, and key challenges. We present a technical characterization of system utilization based on remote monitoring of electricity consumption, power reliability, and power quality at a PUE intervention site, estimating system reliability at 81% over the study period. Lastly, we characterize socio-economic costs and benefits from the productive user's perspective drawing on mixed-method interviews. We find that relatively low amounts of electricity consumption (10–30 kWh per month) command a high revealed willingness to pay (∼$3 per kWh) for the solar-powered displacement of diesel-based welding, significantly improving the unit economics of the deployed system. This analysis and data provides a resource model for the standardization of mini-grid hardware, performance and cost frameworks, and metrics to assess off-grid, under-grid and ultimately grid interactive distributed generation systems. These models are urgently needed to meet the UN Sustainable Development Goal SDG 7 commitment to achieve universal energy access by 2030.

Modern energy services are essential to replace the extensive use of traditional biomass fuels driving several environmental, health, and social issues affecting the welfare of low-income citizens. Particularly, in Colombia, 11% of the households rely on inefficient firewood cooking systems, while two million people have either intermittent access or no access to electricity. This is particularly important in the department of Cordoba, where an average of 32% of the households relies on firewood for cooking, increasing to 66% of the households in rural areas. Furthermore, 20% of the rural population lack access to electricity. Therefore, this study aims at defining the biogas-based energy potential of the available agricultural and manure wastes in the department. To this end, governmental data is used to estimate the demand for firewood for cooking, the resulting GHG emissions, and the available agricultural and manure wastes. Overall, there are around 1.2 million t of agricultural wastes and 2.2 million t of manure yearly available in the department, representing an energy potential of 6687 TJ. Using 26% of the biogas-based energy potential identified suffices to support the 1334 TJ of biogas needed to replace cooking firewood and to supply the 390 TJ needed for household electricity generation. The use of biogas can reduce GHG emissions to 11% of the emissions resulting from cooking firewood. Polyethylene tubular digesters appear as the most indicated household technology, contrasted to geomembrane tubular digesters that need 2.4 times the initial capital investment while fixed dome digesters need 7.9 times the initial capital investment. Implementing household digesters to support the energy demand for cooking in the department, necessitates a minimum of 18 million USD, while the implementation of ‘digester + electric generator’ needs between 1.7 and 5.7 million USDdepending on the monthly demand of electricity of 60 kWh or 187 kWh.

Access to safe drinking water is still very low among the poorest households in sub-Saharan Africa, and economic shocks can make water access even more difficult for poor consumers. Water subsidies can be a solution to enhance access to safe water services, but they are often ineffective as they regularly fail to reach the very poor. In this study, we developed a new Machine Learning-based proxy means test (ML-based PMT) to identify the poorest households and field-tested it in comparison to four other methods (the Demographic and Health Survey (DHS) wealth index, the Poverty Probability Index (PPI), Community Based Targeting (CBT) and the Ghana Government's Livelihood Empowerment Against Poverty (LEAP) program). We first developed our new ML-based PMT by applying machine learning techniques to the nationally-representative 2016–2017 Ghana Living Standards Survey and compared its performance with an existing PMT (the PPI). We then compared the strengths and weaknesses of this new method in three rural towns of southwestern Ghana against the four other methods, with respect to the characteristics of households they identified, their ease of implementation, their cost, and their acceptability among local stakeholders. In our field assessment we found that our new ML-based PMT performed better than most other approaches at screening out households having assets associated with wealth, but it had higher implementation costs than CBT and LEAP. Local government officials considered CBT to be more transparent than the PMTs, while community members perceived the PMTs to be fairer.

By highlighting the strengths and weaknesses of five different targeting methods, this study provides guidance to practitioners in choosing the most appropriate methods to target poor households eligible for water subsidies in rural Ghana.