• Energy Research

Abstract Supercritical water oxidation (SCWO) is considered to be a green technology, providing an effective route to convert waste materials into simpler, less hazardous products. This article reports the use of a physical modelling approach to assess mixing dynamics inside three different types of reactor where supercritical water (water above 374 °C and 218 atmospheres) is mixed with a second colder, waste containing, effluent flow. Physical or ‘pseudo’ modelling was used to simulate the general flow patterns and mixing regimes in transparent pseudo reactors (to allow visualization). Towns water was used to simulate the supercritical water flow (density 998 kg/m3 and viscosity 1.0 × 10 − 3 kg/m s at 25 °C and 1 bar respectively) and 40% w/w aqueous sucrose solution to simulate the cold aqueous effluent flow (density 1176 kg/m3, viscosity 6.16 × 10 − 3 kg/m s at 25 °C and 1 bar respectively). Flow rates of 100’s ml min−1–1000’s ml min−1 were used to create a range of Reynolds numbers experienced during mixing at supercritical conditions (in laminar and turbulent regimes). Three types of vertical pipe-in-pipe reactor were simulated using this method (counter current and co-current arrangements). This visual technique allowed the quantification of mixing efficiency, as well as identification of issues such as flow recycling, stagnant zones, and other inconsistencies in the mixing dynamics. An upwards co-current arrangement provided the ‘best’ mixing i.e. with minimal wall contact during the downstream oxidation process.

Abstract Supercritical water oxidation (SCWO) is considered to be a green technology, providing an effective route to convert waste materials into simpler, less hazardous products. This article reports the use of a physical modelling approach to assess mixing dynamics inside three different types of reactor where supercritical water (water above 374 °C and 218 atmospheres) is mixed with a second colder, waste containing, effluent flow. Physical or ‘pseudo’ modelling was used to simulate the general flow patterns and mixing regimes in transparent pseudo reactors (to allow visualization). Towns water was used to simulate the supercritical water flow (density 998 kg/m3 and viscosity 1.0 × 10 − 3 kg/m s at 25 °C and 1 bar respectively) and 40% w/w aqueous sucrose solution to simulate the cold aqueous effluent flow (density 1176 kg/m3, viscosity 6.16 × 10 − 3 kg/m s at 25 °C and 1 bar respectively). Flow rates of 100’s ml min−1–1000’s ml min−1 were used to create a range of Reynolds numbers experienced during mixing at supercritical conditions (in laminar and turbulent regimes). Three types of vertical pipe-in-pipe reactor were simulated using this method (counter current and co-current arrangements). This visual technique allowed the quantification of mixing efficiency, as well as identification of issues such as flow recycling, stagnant zones, and other inconsistencies in the mixing dynamics. An upwards co-current arrangement provided the ‘best’ mixing i.e. with minimal wall contact during the downstream oxidation process.

Supercritical water gasification is a promising technology for renewable hydrogen production from high moisture content biomass. This work produces a machine learning surrogate model to predict the Levelised Cost of Hydrogen over a range of biomass compositions, processing capacities, and geographic locations. The model is published to facilitate early-stage economic analysis (doi.org/10.6084/m9.figshare.22811066). A process simulation using the Gibbs reactor provided the training data using 40 biomass compositions, five processing capacities (10–200 m3/h), and three geographic locations (China, Brazil, UK). The levelised costs ranged between 3.81 and 18.72 $/kgH2 across the considered parameter combinations. Heat and electricity integration resulted in low process emissions averaging 0.46 kgCO2eq/GJH2 (China and Brazil), and 0.37 kgCO2eq/GJH2 (UK). Artificial neural networks were most accurate when compared to random forests and support vector regression for the surrogate model during cross-validation, achieving an accuracy of MAPE: 0.99 on the test set.

Supercritical water gasification is a promising technology for renewable hydrogen production from high moisture content biomass. This work produces a machine learning surrogate model to predict the Levelised Cost of Hydrogen over a range of biomass compositions, processing capacities, and geographic locations. The model is published to facilitate early-stage economic analysis (doi.org/10.6084/m9.figshare.22811066). A process simulation using the Gibbs reactor provided the training data using 40 biomass compositions, five processing capacities (10–200 m3/h), and three geographic locations (China, Brazil, UK). The levelised costs ranged between 3.81 and 18.72 $/kgH2 across the considered parameter combinations. Heat and electricity integration resulted in low process emissions averaging 0.46 kgCO2eq/GJH2 (China and Brazil), and 0.37 kgCO2eq/GJH2 (UK). Artificial neural networks were most accurate when compared to random forests and support vector regression for the surrogate model during cross-validation, achieving an accuracy of MAPE: 0.99 on the test set.

AbstractProcess simulation for a single‐step synthesis of dimethyl ether (DME) based on the CO2‐enhanced gasification of rice straw was conducted using Aspen Plus. The process consists of a gasification unit, a heat recovery unit, a gas purification unit, a single‐step DME synthesis unit, and a DME separation unit. In the simulation, highly pure DME was produced by the control of CO2 concentration in syngas to a very low level prior to synthesis. A gasification system efficiency of 36.7 % and CO2 emission of 1.31 kg kgDME−1 were achieved. This bio‐DME production based on the CO2‐enhanced gasification of biomass was cost‐effective as it required 19.6 % less biomass than that of DME production based on conventional biomass gasification. The performance and environmental benefits of the proposed process could be further improved by the utilization of unreacted gases and the managing of CO2 generated by the incorporation of a polygeneration concept or carbon storage.

AbstractProcess simulation for a single‐step synthesis of dimethyl ether (DME) based on the CO2‐enhanced gasification of rice straw was conducted using Aspen Plus. The process consists of a gasification unit, a heat recovery unit, a gas purification unit, a single‐step DME synthesis unit, and a DME separation unit. In the simulation, highly pure DME was produced by the control of CO2 concentration in syngas to a very low level prior to synthesis. A gasification system efficiency of 36.7 % and CO2 emission of 1.31 kg kgDME−1 were achieved. This bio‐DME production based on the CO2‐enhanced gasification of biomass was cost‐effective as it required 19.6 % less biomass than that of DME production based on conventional biomass gasification. The performance and environmental benefits of the proposed process could be further improved by the utilization of unreacted gases and the managing of CO2 generated by the incorporation of a polygeneration concept or carbon storage.

Il existe un nombre important d'incendies de mines de charbon incontrôlés (principalement dus à la combustion spontanée du charbon), qui brûlent actuellement dans le monde entier. Ces sources de combustion spontanée émettent des gaz à effet de serre (GES). Un examen critique révèle qu'il n'existe pas de méthodes de mesure standard pour estimer les émissions de GES provenant des incendies de mines/des zones de combustion spontanée. L'objectif de ce document de recherche était d'estimer les émissions de GES provenant de la combustion spontanée des charbons dans le contexte indien. Une méthode de chambre d'échantillonnage (SC) a été utilisée avec succès pour évaluer les émissions à deux endroits du projet Enna Opencast (OCP), Jharia Coalfield (JCF), pendant 3 mois. L'étude révèle que le taux d'émission moyen cumulé mesuré pour le CO2 varie de 75,02 à 286,03 gs−1m−1 et que le CH4 varie de 41,49 à 40,34 gs−1m−1 pour les zones à basse et moyenne température. Les émissions totales de GES prévues à partir de cet incendie unique affectant les mines de JCF varient de 16,86 à 20,19 Mtyr−1. Hay un número significativo de incendios incontrolados en las minas de carbón (principalmente debido a la combustión espontánea de carbón), que actualmente se están quemando en todo el mundo. Estas fuentes de combustión espontánea emiten gases de efecto invernadero (GEI). Una revisión crítica revela que no existen métodos de medición estándar para estimar las emisiones de GEI de las áreas de incendio/combustión espontánea de minas. El objetivo de este trabajo de investigación fue estimar las emisiones de GEI de la combustión espontánea de carbones en el contexto indio. Se utilizó con éxito un método de cámara de muestreo (SC) para evaluar las emisiones en dos ubicaciones del Enna Opencast Project (OCP), Jharia Coalfield (JCF), durante 3 meses. El estudio revela que la tasa de emisión media acumulada medida de CO2 varía de 75.02 a 286.03 gs−1m−1 y CH4 varía de 41.49 a 40.34 gs−1m−1 para zonas de baja y media temperatura. Las emisiones totales de GEI previstas de este único incendio que afecta a las minas de JCF varían de 16,86 a 20,19 Mtyr−1. There are a significant number of uncontrolled coal mine fires (primarily due to spontaneous combustion of coal), which are currently burning all over the world. These spontaneous combustion sources emit greenhouse gases (GHGs). A critical review reveals that there are no standard measurement methods to estimate GHG emissions from mine fire/spontaneous combustion areas. The objective of this research paper was to estimate GHGs emissions from spontaneous combustion of coals in the Indian context. A sampling chamber (SC) method was successfully used to assess emissions at two locations of the Enna Opencast Project (OCP), Jharia Coalfield (JCF), for 3 months. The study reveals that measured cumulative average emission rate for CO2 varies from 75.02 to 286.03 gs−1m−1 and CH4 varies from 41.49 to 40.34 gs−1m−1 for low- and medium-temperature zones. The total GHG emissions predicted from this single fire affecting mines of JCF vary from 16.86 to 20.19 Mtyr−1. هناك عدد كبير من حرائق مناجم الفحم غير المنضبطة (ويرجع ذلك في المقام الأول إلى الاحتراق التلقائي للفحم)، والتي تحترق حاليًا في جميع أنحاء العالم. تنبعث من مصادر الاحتراق التلقائي هذه غازات الدفيئة (GHG). تكشف المراجعة النقدية أنه لا توجد طرق قياس قياسية لتقدير انبعاثات غازات الدفيئة من مناطق حرائق المناجم/الاحتراق التلقائي. كان الهدف من هذه الورقة البحثية هو تقدير انبعاثات غازات الدفيئة الناتجة عن الاحتراق التلقائي للفحم في السياق الهندي. تم استخدام طريقة غرفة أخذ العينات (SC) بنجاح لتقييم الانبعاثات في موقعين من مشروع Enna Openencast (OCP)، Jharia Coalfield (JCF)، لمدة 3 أشهر. تكشف الدراسة أن متوسط معدل الانبعاث التراكمي المقاس لثاني أكسيد الكربون يتراوح من 75.02 إلى 286.03 جم-1 م-1 و CH4 يتراوح من 41.49 إلى 40.34 جم-1 م-1 للمناطق ذات درجات الحرارة المنخفضة والمتوسطة. يتراوح إجمالي انبعاثات غازات الدفيئة المتوقعة من هذا الحريق الفردي الذي يؤثر على مناجم JCF من 16.86 إلى 20.19 متر -1.