The high level of potassium compounds in Empty Fruit Bunch (EFB) induces ash-related problems, such as bed agglomeration, which is caused by the formation of a low-melting-point sticky compound: K₂On·SiO₂, especially in fluidised bed gasification using silica sand as bed material. Dolomite was found to be an effective alternative bed material for preventing bed agglomeration by the release of CaO via calcination processes during gasification. CaO acts as a catalyst to inhibit bed agglomeration by possibly enhancing the formation of K₂CO₃ instead of K₂O·nSiO₂. Alumina sand was also found to be a suitable alternative bed material to prevent bed agglomeration; however, due to the relatively high density of alumina sand, high gas velocity was needed to ensure good mixing and fluidisation. Using both dolomite and alumina sand as bed materials yielded a product gas having similar higher heating value (HHV) to that when using silica sand (i.e., 3.8–3.9 MJ/Nm³).

Palm empty fruit bunches (EFB) is known as problematic biomass due to its high alkali content, i.e., more than half of inorganic matter is potassium (K). EFB when used as a fuel in fluidized beds with silica sand as bed material could form the sticky compound K₂O·nSiO₂ starting at around 750 °C and adhere bed particles together, resulting in bed agglomeration. Blending EFB with rubber wood sawdust (RWS) could improve the chemical properties and consequent ash composition of the blended fuel. In this study, RWS was blended with EFB at three ratios: RWS:EFB = 25:75, RWS:EFB = 50:50, and RWS:EFB = 75:25. Adding RWS to the fuel prolonged de-fluidization time. The high content of CaO in the RWS ash acted as an inhibitor to prevent the formation of K₂O·nSiO₂ and, instead, enhanced the formation of K₂CO₃, a higher melting point compound, which reduced bed agglomeration. During the experiment using RWS:EFB = 75:25, no bed agglomeration was found.

This study proposes a novel two-phase model framework for designing sustainable biomass supply chains of Community-Scale Biomass Power Plants (CSBPPs) by optimization based on geospatial-based Multi-criteria Decision Making (MCDM), the Analytic Hierarchy Process (AHP) method and the Location–Allocation Model. Phase I involved land suitability criteria prioritization and suitable land area analysis. The location–allocation model was the main tool used in Phase II to identify optimal locations, followed by the analysis of the levelized cost of electricity (LCOE). The model optimized site location based on the availability (remaining) of local crop residues, electricity demand, road networks and other key criteria for power plant development, such as the location of substations and the location of existing power plants. The results show that the estimated total remaining crop residue potential in the EEC region was 2403 kt/year, which can generate approximately 34,156 TJ. The location–allocation model identified the top five locations for CSBPPs. The total required installed capacity of these five locations was approximately 100.23 MW in order to serve the district energy demand by the residential sector of 793.82 million (kWh/year). Assuming direct combustion-steam turbine technology with an installed capacity of 6–10 MW, the average LCOE was found to be in a range of $0.076 to $0.081 USD/kWh.

Community-scale biomass power plants (CSBPPs) offer a decentralized approach for electricity generation by utilizing locally available biomass while delivering socioeconomic benefits. Site selection plays a critical role in the success of CSBPPs and requires the consideration of diverse spatial and non-spatial factors. This study presents a spatial decision-support tool for identifying suitable CSBPP sites in Thailand’s Eastern Economic Corridor (EEC), which comprises the Chachoengsao, Chonburi, and Rayong provinces. A geoprocessing workflow integrating Geographic Information Systems (GISs), Multi-Criteria Decision-Making (MCDM), and the Analytic Hierarchy Process (AHP) was developed using ModelBuilder tools in ArcGIS Pro (version 3.0.2). Thirteen sub-criteria related to geographical, infrastructural, and socioeconomic–cultural dimensions, along with exclusion zones, were evaluated by 15 experts from diverse stakeholder groups. Biomass availability from five major economic crops was combined with other spatial data layers, incorporating expert-assigned weights and suitability scores. The findings indicated a remaining biomass energy potential was 34,156 TJ, with sugarcane residues contributing over 80%. Approximately 20% of the EEC area (about 0.262 million hectares) was classified as highly suitable for CSBPP development, revealing several viable site options. The proposed model offers a flexible and replicable framework for regional biomass planning and can be adapted to other locations by adjusting the criteria and integrating optimization techniques.