Kinetic Models for Drying Techniques—Food Materials

Drying operations can help in reducing the moisture content of food materials for avoidance of microbial growth and deterioration, for shelf life elongation, to minimize packaging and improving storage for easy transportation. Thin-layer drying of materials is necessary to understand the fundamental transport mechanism and a prerequisite to successfully simulate or scale up the whole process for optimization or control of the operating conditions. Researchers have shown that to rely solely on experimental drying practices without mathematical considerations for the drying kinetics, can significantly affect the efficiency of dryers, increase the cost of production, and reduce the quality of the dried product. An effective model is necessary for the process design, optimization, energy integration and control;hence, the use of mathematical models in finding the drying kinetics of agricultural products is very important. The statistical criteria in use for the evaluation of the best model(s) has it that coefficient of determination (R2) has to be close to unity while the rest statistical measures will have values tending to zero. In this work, the essence of drying using thin-layer, general approaches to modeling for food drying mechanisms thin layer drying models and optimization of the drying processes have been discussed.

Algorithms for the Optimization of Well Placements—A Comparative Study

The Artificial Bee Colony (ABC) is one of the numerous stochastic algorithms for optimization that has been written for solving constrained and unconstrained optimization problems. This novel optimization algorithm is very efficient and as promising as it is;it can be favourably compared to other optimization algorithms and in some cases, it has been proven to be better than some known algorithms (like Particle Swarm Optimization (PSO)), especially when used in Well placement optimization problems that can be encountered in the Petroleum industry. In this paper, the ABC algorithm has been modified to improve its speed and convergence in finding the optimum solution to a well placement optimization problem. The effects of variations of the control parameters for both algorithms were studied, as well as the algorithms’ performances in the cases studied. The modified ABC (MABC) algorithm gave better results than the Artificial Bee Colony algorithm. It was noticed that the performance of the ABC algorithm increased with increase in the number of its optimization agents for both algorithms studied. The modified ABC algorithm overcame the challenge posed by the use of uniformly generated random numbers with very rough NPV surface. This new modified ABC algorithm proposed in this work will be a great tool in optimization for the Petroleum industry as it involves Well placements for optimum oil production.

Mathematical Modelling and Design of Helical Coil Heat Exchanger for Production of Hot Air for Fluidized Bed Dryer

In global industrialization, efforts have been made to increase the rate of heat transfer in heat exchanger, minimizing the size of heat exchanger to reduce cost as well as increasing the effectiveness. Helical coil heat exchanger (HCHE) has been proven to be effective in improving heat transfer due to its large surface area. In this study, HCHE was designed to provide hot air needed for fluidized bed drying processes. The HCHE design model was fabricated and evaluated to study the efficiency of the hot air output for a laboratory fluidized bed dryer. The mathematical model for estimation of the final (output) temperature of air, Taf, passing through the HCHE was developed and validated experimentally. The drying of bitter kola particulates was carried out with a drying temperature of 50C 3C and a bed height-to-bed diameter ratio (H/D) of 1.5. The time taken to dry bitter kola particulates to 0.4% moisture content was 1 hour 45 minutes. Hence, HCHE is recommended for use in the production of hot for laboratory-scale fluidized bed dryers.