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Napier grass (Pennisetum purpureum), represents an interesting substrate for biogas production. The research project evaluated biogas potential production from dry anaerobic digestion of Napier grass using batch experiment. To enhance the biogas production from ensiled Napier grass, thermal and alkaline pre-treatments were performed in batch mode. Alkali hydrolysis of Napier grass was performed prior to batch dry anaerobic digestion at three different mild concentrations of sodium hydroxide (NaOH). The study results confirmed that NaOH pretreated sample produced high yield of biogas than untreated (raw) and hot water pretreated samples. Napier grass was used as the mono-substrate. The biogas composition of carbon dioxide (30.10%), methane (63.50%) and 5 ppm of HS was estimated from the biogas. Therefore, fast-growing, high-yielding and organic matter-enriched of Napier grass was promising energy crop for biogas production.

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The large number of wastewaters are generated because of the various production processes. Vegetable and fish processing can be considered an important industry for wastewater generation. The essential method for completing this waste is to digest the organic matter using anaerobic digestion followed by aerobic wastewater treatment processes; however, wastewater from tilapia culture pond retains considerable quantities of inorganic substances, particularly nutrients like nitrogen and phosphorus. The optimal conditions for cultivating Chlorella vulgaris from wastewater treatment effluent from tilapia culture pond were investigated in this study. The appropriate conditions were found to be 10% initial stock suspension, 20 cm depth, and 12 days of culture conditions. C. vulgaris had an optical density of 0.649, a cell density of 17.68 × 105 cells/mL, and biomass of 0.376 ± 94.21 mg/L after cultivation. Discharged wastewater from the fishpond was utilized for the improved growth of microalgae and obtained biomass was used for bioethanol production. This study verified that fishpond wastewater is the best source of nutrients for algal mass production and biofuel applications.

Renewable energy resources of part of the Asian region are not only able to fight against climate change issues but also could contribute to economic growth, employment, and energy safety. Biogas production and use are generally regarded as a sustainable practice that can guarantee high greenhouse gas savings. Thailand is an agricultural area suitable for growing of many plants, especially annual crops that can be used as an energy crop or raw material for biogas plant. In addition, grassland biomass is suitable in numerous ways for producing energy and is the most common material for producing biogas in the present scenario. There are several types of grasses popularly growing in Thailand. Grasses are converted to silage which will be used as feedstock for anaerobic digestion. Consequently, this chapter addresses the advances in silage preparations and utilization for efficient biogas production with several digestion methods including dry and wet fermentation processes, monodigestions, and codigestions.

Aside from smart technologies, farm data collection is also important for smart farms including farm environment data collection and farmer survey data collection. With farm data collection, we observe that it is generally proposed to utilize in smart farm systems. However, it can also be released for use in the outside scope of the data collecting organization for an appropriate business reason such as improving the smart farm system, product quality, and customer service. Moreover, we can observe that the farmer survey data collection often includes sensitive data, the private data of farmers. Thus, it could lead to privacy violation issues when it is released. To address these issues in the farmer survey data collection, an anatomization model can protect the users' private data that is available in farmer survey data collection to be proposed. However, it still has disorganized issues and privacy violation issues in the sensitive table that must be addressed. To rid these vulnerabilities of anatomization models, a new privacy preservation model based on data shuffing is proposed in this work. Moreover, the proposed model is evaluated by conducting extensive experiments. The experimental results indicate that the proposed model is more efficient than the anatomization model for the farmer survey data collection. That is, the adversary can have the confidence for re-identifying every sensitive data that is available in farmer survey data collection that is after satisfied by the privacy preservation constraint of the proposed model to be at most 1/l. Furthermore, after the farmer survey data collection satisfies the privacy preservation constraint of the proposed model, it does not have disorganized issues and privacy violation issues from considering the sensitive values.

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