MATHEMATIC MODEL OF HIDROLYSIS PROCESS FROM BANANA TREES CELLULOSE TO GLUCOSE BY USING LIQUID ACID CATALYST

 Banana trees are rich of cellulose and a potential source for producing glucose. The aim of the research was to develop a mathematic model of banana tree fiber hydrolysis by using sulfite acid as catalyst. The concentratios of the acid catalyst were 0.25 M, 0.5 M, and 1M at temperature of 85 oC and 100 oC. The concentration of acid catalyst and the reaction temperature were varied to determine the activation energy that might be affected by the addition of acid catalyst. The sugar concentration was measured as an hydrolysis parameter. The concentration of reducing sugar produced during the hydrolysis process was also measured to develop the mathematical model of hydrolysis process. The values of determination coefficient (r2) from mathematical model of kinetic reaction were more than 0.8. The kinetic model was valid for concentration range from 0.25 M to 1.5 M. Keywords: mathematical model, determination coefficient, hydrolysis, banana trees, liquid acid catalys

RESEARCH AND DEVELOPMENT FOR CASSAVA BASED INDUSTRY

 Cassava, locally known as ubi kayu, singkong or ketela pohon, has huge potency due to: (i) can be grown in almost all climate conditions, (ii) drough resistence, (iii) raw material for many uses vary from food, feed to energy, and (iv) technically well known by community. However, there is no strong cassava based processing industry (tapioca, cassava flour, glucose syrup, and ethanol) grow with good business insight. This review sees the gap comprehensively between the capacity to produce and availability of raw material (fresh cassava), and high potency of uses and increasingly down stream product demand. This paper ends with recommendations on how to bridge upstream potency and down stream demand through the development of cassava based industry. Special discussion on Cassava Research Center is provided. Keywords:   Cassava Research Center, development, food security, biofue

AN INTELLIGENT DECISION SUPPORT SYSTEM FOR HORTICULTUE

 The objective of this research was to develop an intelligent decision support system for optimization of horticulture supply chain model using genetic algorithms. The case study was conducted at PT. Saung Mirwan, Megamendung-Bogor, a major producer of packed fresh vegetable and fresh-cut vegetable. The output of this research is an Intelligent Decision Support System of Supply Chain Management for Horticulture Agro industry (IDSS-SCM). IDSS-SCM consists of eight models: Products Demand Forecast, Vegetables Supply Forecast, Planting Schedule, Aggregate Planning, Material Requirements Planning I, Material Requirements Planning II, Inventory Management, and Transportation Route. Based on the most recent data collected, IDSS-SCM predicts that product demand will increase and it then gives optimum recommendations to the user such as plant schedule, material requirements planning, inventory, human resource allocation, and distribution route to fulfil the demand. The unique feature of this research was that a genetic algorithm (GA) with Partially Matched Crossover (PMX) operator was used to find the shortest distribution route as well as to optimize human resource allocation problem. The experiment results indicate that the GA developed in this research can solve a complex agroindustrial supply chain design problem faster and more efficiently. Keywords: intelligent decision support system, genetic algorithms, supply chain management, agroindustry, partially matched crossover