Abstract. Biogas is made from the anaerobic process, which methane (CH4) as a primary element with some other elements. The composition of biogas in the experiment still contains H2S gas pollutants by 41,76 ppm. The amount of H2S content can reduce CH4 composition, so it has a lot of efforts to reduce H2S in biogas. One of them used water scrubber system, but the water scrubber system always changed according to the temperature ambient. It can cause inconsistency biogas quality, which is the reduction of H2S always changed. Another study showed that the speed of the water flow of 5.5 to 11 liters / min and water temperature of 10-25 °C, can reduce the H2S content in biogas up to 4.5 -8%, of the initial content by 37,8% to 43.6%. So that is needed the design of water cooling system on purifying biogas. While this research aims that the lower water temperature will increase the effectiveness of the reduction of H2S with water temperature 15-20 °C. From the experiments that have been carried out test results obtained when the water temperature 20 °C the content of H2S was 0.7 ppm when the water temperature 17°C H2S content was 0.6 ppm when the water temperature 15 ° C H2S was 0.5 ppm.

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AbstrakAbstrak -Saat ini konsumsi energi di Indonesia mengalami peningkatan sehingga pemanfaatan energi terbarukan lebih dikembangkan untuk memenuhi proyeksi kebutuhan energi masa depan. Salah satu sumber energi terbarukan yang sedang dikembangkan penggunaannya adalah biogas, khususnya untuk biogas skala rumah tangga. Implementasi biogas pada skala rumah tangga ada beberapa macam, salah satunya adalah penggunaan biogas sebagai bahan bakar generator untuk menghasilkan listrik. Bahan bakar generator bisa menggunakan biogas secara penuh atau bahan bakar campuran gasoline dengan biogas. Generator set listrik dengan bahan bakar ganda gasoline-biogas dapat menghemat penggunaan gasoline sebagai bahan bakar dan juga dapat menigkatkan performansi generator. Rasio campuran gasoline-biogas berpengaruh terhadap performansi engine, salah satunya pada kecepatan putar. Namun saat ini rasio campuran gasoline dengan biogas masih diatur secara manual pada penggunaan biogas skala rumah tangga. Berdasarkan pada kondisi tersebut, maka dalam penelitian ini dikembangkan metode jaringan syaraf tiruan / artificial neural networks (ANN) yang bertujuan untuk mencari rasio optimal agar mendapatkan karakterisasi kecepatan putar generator set dengan nilai performansi engine terbaik. Sebanyak 300 variasi data diolah menggunakan JST 75% untuk training dengan jumlah hidden node 100 nilai net.trainParam.goal = 0.0001, net.trainParam.lr = 0.01, dan net.trainParam.epochs = 1000, serta 25% untuk uji. Penelitian ini menghasilkan nilai RMSE training sebesar 10,4812 pada node ke 55 dan nilai RMSE uji sebesar 5,8301 dengan hasil kecepatan putar 3445,87, dan mendapatkan rasio terbaik pada gasoline 0,012 L/menit dan biogas 5 L/menit. AbstractAbstract--Currently, energy consumption in Indonesia has increased so that the utilization of renewable energy is more developed to supply projections for future energy needs. One of the renewable energy sources that is being developed is biogas, especially for household-scale biogas. There are several types of biogas implementation at the household scale, one of which is the use of biogas as generator fuel to produce electricity. Fuel generators can use biogas in full or mix gasoline with biogas fuel. Electric generator sets with dual gasoline-biogas fuel can save the use of gasoline as fuel and can also increase the performance of generators. The gasoline-biogas mixture ratio affects engine performance, one of which is the rotational speed. However, at present the ratio of gasoline to biogas is still manually regulated on household scale biogas usage. Based on these conditions, the artificial neural networks (ANN) method was developed in this study which aims to find the optimal ratio in order to get the generator set rotational speed characterization with the best engine performance value. A total of 300 variations of data were processed using 75% for training with the number of hidden nodes 100 net.trainParam.goal value = 0.0001, net.trainParam.lr = 0.01, and net.trainParam.epochs =

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