12 research outputs found

    Life Cycle Assessment of Sugar from Sugarcane: A Case Study of Indonesia

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    Facing rising environmental concerns in the industry sector, especially sugar industry, requires to assess environmental performances. Minimizing the environmental impact like eco design is vital to save the earth. Life cycle assessment (LCA) is applied to know the impact of sugar production from agricultural stage to industrial stage, without considering their usage and disposal phases (cradle to gate). Eco Indicator 99 (H) is used as life cycle impact assessment (LCIA) methods in this study. Life cycle of sugar from sugar cane in Kebon Agung sugar company, East Java province, Indonesia is analyzed by SimaPro 7.3.3 software. Agricultural stage especially fertilizer usage is the largest source of emission in the whole sugar processing with dominant emissions in carcinogens and land use categories. It contributed 86% of whole impact. The contribution from de-ionsed water and burnt lime are also significant to give emission during industrial stage. It mainly affected respiratory inorganics impact category.

    A new conceptual approach for systematic error correction in CNC machine tools minimizing worst case prediction error

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    A new artifact-based method to identify the systematic errors in multi-axis CNC machine tools minimizing the worst case prediction error is presented. The closed loop volumetric error is identified by simultaneously moving the axes of the machine tool. The physical artifact is manufactured on the machine tool and later measured on a coordinate measuring machine. The artifact consists of a set of holes in the machine tool workspace at locations that minimize the worst case prediction error for a given bounded measurement error. The number of holes to be drilled depends on the degree of the polynomials used to model the systematic error and the number of axes of the machine tool. The prediction error is also function of the number and location of the holes. The feasibility of the method is first investigated for a two-axis machine to find the best experimental setting. Finally based on the two-axis case study, we extend the results to machine tools with any number of axes. The obtained results are very promising and require only a short time to produce the artifac

    A New Optimal Selection Method with Seasonal Flow and Irrigation Variability for Hydro Turbine Type and Size

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    A micro hydropower plant of the run-of-river type is considered to be the most cost-effective investment in developing counties. This paper presents a novel methodology to improve flow estimation, without using the flow direction curve (FDC) method, to determine the turbine type and size to operate consistently. A higher precision is obtained through the use of seasonal flow occurrence data, irrigation variability, and fitting the best probability distribution function (PDF) using flow data. Flow data are grouped in classes based on the flow rate range. This method will need a larger dataset but it is reduced to a tractable amount by using the PDF. In the first part of the algorithm, the average flow of each range is used to select the turbine type. The second part of the algorithm determines the optimal size of the turbine type in a more accurate way, based on minimum and maximum flow rates in each class range instead of the average flow rate. A newly developed micro hydropower plant was installed and used for validation at Baan Khun Pae, Chiang Mai Province. It was found, over four years of observation from 2014⁻2018, that the plant capacity factor was 82%
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