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Abstract Renewable energy is expected to gain a larger share in the Indonesian energy mix by 2025. Biogas has not only the potential to contribute significantly to the country's renewable energy targets by mid-century, but also to provide a series of co-benefits to improve wellbeing of vulnerable rural communities. The challenge is to find pathways for biogas to overcome well-established technologies and practices in the current heavily fossil fuel based energy system. This paper focused on household-level biogas development in Bali, Indonesia, critically analysing four domestic biogas programmes that are currently under implementation. For our analysis, we adopted an integrated approach combining the multi-level perspective framework with a system-mapping exercise. Moreover, a series of semi-structured interviews as well as an interactive workshop was conducted which allowed us to place multiple stakeholder perspectives and experiences at the heart of the analysis to look at factors beyond the micro-system of the technology, and understand the broader socio-institutional context. Our analysis of enabling and hindering factors to biogas expansion identified opportunities for improving the design and implementation of policy, finance and technology measures. These findings are relevant not only for the Indonesian context, but also for other developing countries that are increasingly investing in transitioning to low-carbon energy futures. Highlights • Barriers to biogas adoption mainly found in policy sector, access to finance and capacity building. • Overcoming barriers is possible by improving steps in market chain and with right policies. • Focusing on co-benefits, creating ownership and build capacity amongst adopters will be crucial. [ABSTRACT FROM AUTHOR]

Several recent life cycle assessments (LCA) of biomass densification have been carried out. This paper reviews data from 19 sources with 48 case scenarios to assess the current status of LCA of biomass densification. It describes the specific units in a reference “gate-to-gate” LCA in relation to the existing studies, and summarises key differences between them. Finally, it provides a qualitative analysis of the associated sources of uncertainty. Existing LCA studies of biomass densification were found to provide insufficient and inconsistent information for full transparency and comparability, due to different choices in system boundary, functional unit, allocation procedure, densification technology and biomass residues. Most of the reviewed studies attributed most of the energy use and greenhouse gas (GHG) emissions to transportation, drying and densification. The energy and GHG emissions of the gate-to-gate densification system were highly sensitive to the technology, feed material used in densification and scale of production. Apart from one study with zero energy consumption as a result of the use of manual operations, the normalised values of energy consumption for the reviewed studies ranged from 20 to 900 kJ MJ −1 . Neglecting three outlier values, GHG emissions as mass of CO 2 -eq for the reviewed studies ranged from 600 t MJ −1 to 50 g MJ −1 . Similar variations in result and outlier cases have beesn reported for other bioenergy processes, by other authors. Assuming that the biggest impact of densification processes is on transport fuel use, and based on 5 studies that reported densification ratios, the net energy and GHG emissions savings resulting from densification ranged from 200 to 1000 kJ MJ −1 and 9 to 50 CO 2 -eq (g MJ −1 ), respectively. On this basis, it can be concluded that biomass densification is a worthwhile addition to the biomass energy conversion system. There is a need for more transparent reporting and analysis of uncertainty in the modelling, to better understand the wide variation in outcomes. [ABSTRACT FROM AUTHOR]

This paper presents a method using an artificial neural network for creating an optimal model of energy consumption in wastewater treatment plant (WWTP) using activated sludge process. The advantage of this method is the use data usually measured in most of WWTP to optimize the energy consumption of the biological process. This method consists of selecting the data that represent the best energy consumption using different performance criteria then use this data to train a deep neural network. The procedure of selection is divided into two parts, in the first selection we selected the data that respect the environmental standards, and in the second part we selected the data with optimal energy consumption using different pollution indicators, and this data was used to train a deep neural network, finally the best model was used to estimate the energy savings on the data not selected. The model showed good results with a coefficient of determination that varies between 90% and 92% in training period and 74%–82% in testing period, the application of the best model on the data not selected showed a gain in energy for the most of the data. • High values of performance criteria in training and testing period • Determination of three classes of energy consumption: Underconsumption, overconsumption and optimal consumption. • Determination of warning limits of overconsumption of energy. [ABSTRACT FROM AUTHOR]