Rice husk power

by Susan Reidy
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Rice milling companies spend more than half of their operating costs on thermal energy used for drying and electricity.

Reducing those costs and increasing profitability is possible with an optimal rice mill utility system that efficiently uses rice husks as the main byproduct of rice milling and as an important source of renewable energy, according to a paper written by Lim Jeng Shiun, Haslenda Hashim, Zainuddin Abdul Manan and Sharifah Rafidah Wan Alwi in the Industrial & Engineering Chemistry Research journal.

The paper presented a mathematical approach to determine the optimal logistic network for the rice husk supply; the economic scale of the rice husk cogeneration system; and an optimal utility supply network for a series of dryers consisting of a combination of cyclonic husk furnace (CHF) and co-generation systems.

Using the formula, the authors determined that an optimal configuration includes a specified rice husk logistic network, a 15-tonne boiler for the co-generation system and eight units of CHFs to satisfy the rice mill heat and power requirements.

Such a network will reduce 18.5% of the total rice mill annualized cost, which includes capital, fuel and electricity.

Husk power

Rice husk can be used for power generation, for a cogeneration system and in paddy drying. For drying purposes, the paddy is typically burned in a cyclonic husk furnace to produce hot gas. About 20 kilograms of husk can generate 60,000 to 70,000 kcal of heat, enough to reduce the moisture content of one tonne of paddy from 20% to 14%.

Husks have been used as a fuel for co-generation systems, which must be designed with flexibility to supply electricity for rice milling operations throughout the year, and heat for dryers during harvesting seasons.

During peak drying periods, a co-generation system should provide extensive thermal heat to dry paddy within 72 hours of harvesting in order to preserve quality. At other times of the year, co-generation should be used for electricity generation.

To maximize the economic impact of co-generation and CHF systems, several factors must be considered, the authors noted, including:
•Variation of heat and power demands during different periods of the year. A detailed assessment must be made to exploit the difference in heat and energy demands during the peak and off-peak drying periods.
•Energy supply options. The efficiency and cost-effectiveness of the options are vital to consider for an optimal system.
•Rice husk supply limitation. The amount of rice husk from a mill is limited and may not be able to sustain the heat and power demands of energy intensive milling and drying. There is a need to transport and purchase rice husks from other mills. The limited supply, required transportation and purchasing costs are key factors to consider in the design of a rice mill utility system.

System optimization

The authors arrived at a mathematical approach for optimal planning of a rice husk logistic network and design of a rice mill utility system. The system uses rice husk from various locations to meet the electricity and drying requirements of the rice mill at different times of the year.

They formulated an integrated superstructure that includes all logistics and utility system configurations; transformed the superstructure as a mixed integer linear programming problem; and developed an optimal solution methodology. The method was then tested on a rice mill in Malaysia.

The company planned to install a co-generation system within its centralized drying facility located in its rice mill. It will dry about 100,000 tonnes of paddy per year. A combined CHF and co-generation will be designed to meet the heating requirements of the drying facility and also to generate power. The system is expected to provide cost savings for the company.

The moisture content of the paddy from a country like Malaysia can reach up to 25%, so 4,000 tonnes of rice husks are required to dry 100,000 tonnes of paddy. More rice husks will need to be purchased and transported from other mills.

In a typical rice mill facility that uses rice husk as a fuel, the CHF supplies heat to a dryer system, which may include a combination of fluidized bed dryers (FBD) and inclined bed dryers (IBD). Electricity is supplied by the national grid.

Rice husk is transported from internal and external rice mills. The distance between rice husk supply locations and the co-generation facility affects transportation costs.

Steam is generated when rice husks are burnt as fuel in the boiler. The amount of steam produced depends on the amount of rice husk burnt, its calorific value, the boiler efficiency and the enthalpy change across the boiler. If the electricity produced by the co-generation system can’t meet demand, additional electricity can be purchased from the national grid.

Boiler capacity governs how much steam is generated on an hourly basis. To ensure boiler operability, it must operate above the boiler turndown ratio, which is typically 50% of the boiler maximum capacity.

Case study

The company’s potential rice husk supply network includes 10 internal rice mills and six external rice mills. The husk is transferred from the rice mills to the drying facility. Heat and power comes from a co-generation system for heat and electricity, and CHF for heat for the process dryers.

There are two six-month paddy growing seasons per year. Paddy must be dried to the specified moisture content within 72 hours after harvest. The rice mill peak drying period of 30 days coincides with the harvesting period. No drying is done during the off-peak period since no paddy is being harvested.

During the peak period, the optimal rice husk supply mix is selected from one internal rice mill and three external rice mills, the authors found. But during the off-peak drying period, rice husk came from three internal and two external rice mills. The rice mills are selected as they are able to fulfill the demand at a minimum cost. The optimal utility system includes a 15-tph boiler for the co-generation system and six units of CHFs. The CHFs supply heat to all the FBDs and 16 units of the IBDs. The co-generation system also supplies heat to the remaining IBDs.

When the cost of transportation increases 80% as compared to the baseline value, a co-generation system is no longer favorable, the paper reported. At this point, all heat will be supplied by CHF and electricity will be outsourced.

If the transportation cost increases 60%, the logistic network configuration can remain the same since the boiler size remains unchanged. The overall utility system cost is still lower in comparison to the baseline, because electricity savings derived from co-generation have outweighed the increase in total rice husk cost.

As the electricity tariff increases from the baseline value, the boiler size and the amount of electricity generated also is increased. To minimize the impact of the tariff, co-generation is a favorable option to fulfill heat and power demands. Therefore, more heat loads of IBDs are supplied by the co-generation system.

Unlike CHF, which uses rice husk energy as thermal energy, co-generation uses rice husk energy in thermal and electrical energy. As a result, co-generation tends to consume more rice husk than CHF to supply the same amount of thermal energy. During the off-peak drying period, more rice husks will be required as fuel for the co-generation system to produce electricity.

In a baseline case, eight CHF units are needed. With the use of co-generation, only six units of CHF are needed as the remaining heat is supplemented by the 15-tph co-generation system.

As the drying load increases to 25%, the heat supplied by the co-generation system remains the same, with the extra drying load being supplied by CHF. As the drying load increases from 75% to 100%, the heat and power supplied by the co-generation system are also increased, the authors said.

Designing an optimal rice mill utility system using rice husk biomass is a complex problem that involves trade-offs between various capital and operating costs, the authors said. By combining economics and process data, optimization offers a comprehensive solution to address the complex problem.