Optimal utilization of biomass feedstocks : a case study based on rice and sugar mills in Thailand.

Renewable energy in general, and biofuels in particular, is seen as a viable solution for energy security and climate change problems. For this reason many countries, including Thailand, have set common objectives for utilisation of alternative resources. Thailand is an agricultural country and hence it has a great potential for generating renewable energy from a large amount of biomass resources. In consequence, a 15-year renewable energy development plan has been set by the Thai government, which targets an increase in electricity generation of 32%, from 2,800 MW in 2011 to 3,700 MW in 2022, and also an increase in consumption of ethanol by 200%, from 1,095 million litres in 2011 to 3,285 million litres in 2022 (Department of Alternative Energy Development and Efficiency of Thailand, 2008). Sugarcane and rice are the two main industrial crops in Thailand, with estimated production of 73.50 million tons of sugarcane per year (2009) and 31.50 million tons of rice per year (Sawangphol, 2011), and they are seen as a major source of biomass. This research focuses on the biomass from rice mill and sugar mill processes. In order to develop processing facilities that are capable of utilising available biomass and delivering the above set targets, a comprehensive and systematic methodology is required which will support the decision-making process by accounting for technological, economic and parameters. In this thesis, exhaustive simulation and optimisation are proposed as a tool. The first tool is the technology screening. The aim of the technology screening step is to show all profitability of technologies. This is done by considering various components of rice and sugar mills energy frameworks in Thailand: rice mill technology type, sugar mill technology type, ethanol technology type and biomass based power plant technology type. The modelling of processes for converting sugarcane and rice biomass into electrical energy and ethanol has been performed at the level of superstructure which has been chosen because the scope of the work is to screen available options and to compare them in different configurations in terms of economic aspects. The result of the simulation approach has shown the most profitable (shortest payback period) is the configuration that includes electrical rice mill, automated control sugar mill, gasification biomass based power plant and continuous ethanol plant. The sensitivity analysis has compared the cost of feedstock against profitability (payback period). The sensitivity analysis also compared the price of product against profitability (payback period). The result of the sensitivity analysis showed the change in the price of sugar product is the most sensitive for the rice and sugar mills energy framework. The second tool is the optimisation approach. The aim of the optimisation is to maximise the profit (NPV) impact. This is done by considering the various components of the biofuel supply chain in Thailand. All components were calculated based on candidate points including: the biofield(rice mill and sugar mill), biomass warehouse capacity and location, biofuel plant technology type, plant technology capacity, plant technology location, product warehouse capacity and location, transportation type is considered. There are four scenarios in the case study which were created to examine the proposed biomass optimisation model for Thailand to validate the mathematic formulation. The overall conclusion of the optimization approach is that the biomass power plant is profitable at the present time. The lignocellulosic plant will be the option when the process demand a lot of ethanol production. In summary, the proposed research fills the gap in the operational level and process level by multi-biomass from biofield to customer that includes warehouses and multi transportation modes towards the biofuel supply chain. From the business point of view, the research defines the data for the business investor and also analyses the risk of change in product price, feedstock cost and transportation cost