Optimization of Micro Crystalline Cellulose Production from Corn Cob for Pharmaceutical Industry Investment

MCC （micro crystalline cellulose） is a very important product in pharmaceuticals, foods, cosmetics and other industries. MCC can be made from any natural cellulose materials that have high content of cellulose ranging from pure cellulose, commercial grade cellulose to lignocellulosic materials. In this work, Beeswing （-20 L） and Chaff （5/8） which are the parts of corn cobs were used as raw materials to produce MCC via alkaline degradation, bleaching and hydrolysis. The optimum conditions of alkaline degradation, bleaching and hydrolysis were studied. MCC samples that prepared from -20 L and 5/8 were characterized through XRD （X-ray diffraction）, SEM （scanning electron microscopy） and compared with the commercial MCC （Avicel PH 101）. The results show that the degree of crystallinity of alkaline degradation, bleaching and hydrolysis obtained at 10% of NaOH 95 ~C for 2 h, NaCIO21.5 g 10% of acetic acid 0.5 mL 70 ~C for 2 h, 2 N of HC1, 105 ~C for 1 h showed maximum values which are 77.07%, 75.07% and 86.84%, respectively. The degree of crystallinity and the morphology of prepared MCC correspond to that of Avicel PH 101 industrial investment has been studied, the benefits of micro crystalline cellulose production （MCC） is 3,447 baht/kg. The investment of the plant is 7,263,514 baht and the breakeven point is around 6 years.     

Transition temperature and specific heat jump of a proximity effect sandwich containing local spin fluctuations

The renormalization equations for a proximity effect sandwich containing local spin fluctuations in the normal layer are solved when the temperature is close toT _c . Expressions for the transition temperature and the specific heat jump atT _c are obtained. This decrease of the transition temperature as a function of the thickness of the normal layer is seen in the appropriate limits to be similar to what is expected of sandwiches containing nonmagnetic localized states and of sandwiches containing well-defined magnetic moments. Since no reentrant behavior is seen for the magnetic case, the specific heat jump for this case does not show the increase at low temperatures recently obtained by Kaiser and Keleher for sandwiches containing Kondo impurities.     

Large machine-part family formation utilizing a parallel ART1 neural network

The binary adaptive resonance (ART1) neural network algorithm has been successfully implemented in the past for the classifying and grouping of similar vectors from a machine-part matrix. A modified ART1 paradigm which reorders the input vectors, along with a modified procedure for storing a group's representation vectors, has proven successful in both speed and functionality in comparison to former techniques. This paradigm has been adapted and implemented on a neuro-computer utilizing 256 processors which allows the computer to take advantage of the inherent parallelism of the ART1 algorithm. The parallel implementation results in tremendous improvements in the speed of the machine-part matrix optimization. The machine-part matrix was initially limited to 65,536 elements (256×256) which is a consequence of the maximum number of processors within the parallel computer. The restructuring and modification of the parallel implementation has allowed the number of matrix elements to increase well beyond their previous limits. Comparisons of the modified structure with both the serial algorithm and the initial parallel implementation are made. The advantages of using a neural network approach in this case are discussed.