Simulation of Condensation in Biogas containing Ammonia

Condensation in raw biogas during compression is a problem because the CO2 and water in the liquid phase is very corrosive. Raw biogas typically contains 60 mol-% methane, 40 mol-% CO2, is saturated with water and may contain contaminants as ammonia (NH3). In case of NH3, it is of interest whether it has influence on the dew point (condensation) temperature. The aim of this work is to calculate the dew point under different conditions using different equilibrium models. Phase envelopes showing the two-phase area are also calculated. For dry mixtures of methane and CO2 with up to 1 mol-% NH3 (a high value for biogas), the different models gave similar results. When the NH3 increased from 0 to 1 mol-%, the dew point temperature increased with approximately 3 K. When water was included, the amount of calculated NH3 dissolved in water varied considerably with the model. The electrolyte based models Sour PR, Sour SRK and Electrolyte NRTL did not calculate reasonable dew point temperatures, but the dissolved amounts of NH3 and CO2 were more reasonable using the electrolyte models compared to using PR or SRK. For biogas simulation including NH3, a simple equation of state as PR or SRK can be recommended to determine the dew point. If accurate composition of the condensed liquid is to be calculated, an electrolyte based model like Sour PR, Sour SRK or the Electrolyte NRTL is recommended.publishedVersio

Work Placement in Higher Education – Bridging the Gap between Theory and Practice

During the engineering education, the focus is on theoretical knowledge and less on the practical competence to be a professional engineer. To lead higher education into the future, it is critical to develop models and methods to prepare students for working life. As a mobilizing instrument, work placement is a method to strengthen the flow of knowledge within the innovation systems. Developing knowledge and transversal skills are critical to prepare students for working life. This paper describes the course “Practical Engineering” integrated in the engineering bachelor programs at the University of South-Eastern Norway, Faculty of Technology, Natural Sciences, and Maritime Sciences (USN-TNM). The pilot project started up in 2015 in cooperation with The Research Council of Norway, regional government, industry and USN-TNM. From 2015 until 2018, 70 students have elected the course and more than 40 companies have offered a work placement. Data has been obtained through surveys and student reports from 2017-2018. The evaluation and feedback from both students and companies were satisfactory. In addition to fulfilling purpose and goals for the course, there are also several other positive side effects in the University-Business cooperation

Simulation of dew points in raw biogas using PR and SRK equations of state :

Biogas contains mainly methane, but raw biogas can contain large amounts of CO2 and is normally saturated with water. Condensation, especially during compression, may lead to operational problems. The aim of this work is to calculate the dew point (condensation limit) under different conditions with different models in the simulation programs Aspen HYSYS and Aspen Plus. Binary coefficients for water and CO2 in these models will be fitted to experimental data from the literature. Traditionally, gas mixtures of methane, CO2 and water are calculated with standard models like Peng-Robinson (PR) and Soave-RedlichKwong (SRK). For dry biogas (mixtures with only methane and CO2) all the models give similar results. For a biogas mixture with 60 mol-% methane and 40 mol-% CO2 with 0.1 mol-% added water, the models using binary coefficients fitted for binary mixtures (especially for CO2 and water), gave reasonable results up to about 70 bar, with deviations in the calculated dew point up to 8 K. The binary coefficient for water and CO2 was fitted to experimental data from the literature for a mixture with a CH4 to CO2 molar ratio of 30/70, 50/50 and 70/30. The fitted kij values for the PR model were 0.65, 0.21 and 0.17, respectively. For the SRK model, the kij values were slightly higher. At pressures below 70 bar and temperatures below 40 °C, the uncertainty for calculated dewpoints in mixtures with 30 to 100 % CH4 was reduced to less than 4

Improving quality on health data, recommendations and guidelines - Based on the case of the Health Management Information System in Malawi and DHIS2

A Health Management Information System (HMIS) is an important element for a country’s capacity to monitor health, and for evaluating and improving the delivery of health-care services and programs. Many developing countries still struggle with quality problems in their HMIS data. With this as a point of departure, this project has a twofold ambition; first to propose methods for improving data quality of HMIS data in Malawi, second to gain insight on how the DHIS2 system in general can improve HMIS data. Statistics Norway’s approach is based on well-known quality methods, involving both statistical methods and evaluating structural and institutional challenges. HMIS data in Malawi is collected through the District Health Information System (DHIS2). The project achieved to introduce the new DHIS2 quality tool in Malawi, and thus make modern statistical techniques, methods and tools available. In addition, training and local capacity building in the use of these techniques, methods and tools was carried out. In the process it was quickly surmised that, due to lack of quality in the data collected, several other measures needed to be put in place in order to improve the quality of health data in Malawi. Firstly, there is a lack of central control over the process of collecting health data. Multiple agencies are involved, each with its own agenda and field of interest. There is also little or no coordination between the agencies involved. This institutional context has led to a process where vast amounts of data are collected, but these receive little or no quality control. There is need for an implemented strategy for quality control and central data processing. Furthermore, official publication of results has not been regular and no coherent dissemination procedures are in place. Data providers have minimal training in their field of work and there is a high degree of turnover. One last issue is that there isn’t any feedback to the local health officials reporting the data. This reduces their incentives to improve their reporting. To sum up, Malawi today (2015) has access to the technology and the statistical methods that are associated with the production of data and statistics of sufficient quality (DHIS2 and the quality tool). What is found lacking are institutional factors surrounding and supporting the process. A lot has been said about developing countries’ ability to leap-frog stages in technological development and the opportunities such advances provide. But modern technology and methods often depend on a proper institutional context in order for society to reap the benefits of the new technology. For Malawi to reach their goal of good quality health statistics, the institutional context should be subjected to a deeper analysis. First and foremost, central control over production of statistics by the Ministry of Health should be strengthened. Greater coordination of agencies and reduction in the amount of data collected should be one of the main tasks set for the Ministry of Health. The input side in the data/statistics production chain should receive greater focus. Technology can alleviate some problems with paper-based collection, but several problems remain; lack of training, burdensome amount of data collected and lack of feedback. These problems should be resolved in order to increase data quality. Furthermore, an indepth quality self-assessment should be carried out and an action plan developed to address issues uncovered. The Ministry of Health, through The Central Monitoring and Evaluation Division (CMED) are motivated to improve DHIS2 data quality. CMED has a central role in this project and have been an active contributing partner, characterised by an approach of openness both by sharing data and working routine

Improving quality on health data, recommendations and guidelines - Based on the case of the Health Management Information System in Malawi and DHIS2

A Health Management Information System (HMIS) is an important element for a country’s capacity to monitor health, and for evaluating and improving the delivery of health-care services and programs. Many developing countries still struggle with quality problems in their HMIS data. With this as a point of departure, this project has a twofold ambition; first to propose methods for improving data quality of HMIS data in Malawi, second to gain insight on how the DHIS2 system in general can improve HMIS data. Statistics Norway’s approach is based on well-known quality methods, involving both statistical methods and evaluating structural and institutional challenges. HMIS data in Malawi is collected through the District Health Information System (DHIS2). The project achieved to introduce the new DHIS2 quality tool in Malawi, and thus make modern statistical techniques, methods and tools available. In addition, training and local capacity building in the use of these techniques, methods and tools was carried out. In the process it was quickly surmised that, due to lack of quality in the data collected, several other measures needed to be put in place in order to improve the quality of health data in Malawi. Firstly, there is a lack of central control over the process of collecting health data. Multiple agencies are involved, each with its own agenda and field of interest. There is also little or no coordination between the agencies involved. This institutional context has led to a process where vast amounts of data are collected, but these receive little or no quality control. There is need for an implemented strategy for quality control and central data processing. Furthermore, official publication of results has not been regular and no coherent dissemination procedures are in place. Data providers have minimal training in their field of work and there is a high degree of turnover. One last issue is that there isn’t any feedback to the local health officials reporting the data. This reduces their incentives to improve their reporting. To sum up, Malawi today (2015) has access to the technology and the statistical methods that are associated with the production of data and statistics of sufficient quality (DHIS2 and the quality tool). What is found lacking are institutional factors surrounding and supporting the process. A lot has been said about developing countries’ ability to leap-frog stages in technological development and the opportunities such advances provide. But modern technology and methods often depend on a proper institutional context in order for society to reap the benefits of the new technology. For Malawi to reach their goal of good quality health statistics, the institutional context should be subjected to a deeper analysis. First and foremost, central control over production of statistics by the Ministry of Health should be strengthened. Greater coordination of agencies and reduction in the amount of data collected should be one of the main tasks set for the Ministry of Health. The input side in the data/statistics production chain should receive greater focus. Technology can alleviate some problems with paper-based collection, but several problems remain; lack of training, burdensome amount of data collected and lack of feedback. These problems should be resolved in order to increase data quality. Furthermore, an indepth quality self-assessment should be carried out and an action plan developed to address issues uncovered. The Ministry of Health, through The Central Monitoring and Evaluation Division (CMED) are motivated to improve DHIS2 data quality. CMED has a central role in this project and have been an active contributing partner, characterised by an approach of openness both by sharing data and working routinespublishedVersio