CONFERENCE OF SPECIALIZED RENEWABLE ENERGY, CLEAN AND EFFICIENT | Year:2014 | دوره:5 |Papers Count:2

LIGNOCELLULOSIC MATERIALS ARE ABUNDANT AND RENEWABLE FEEDSTOCKS OF BIOENERGY WHICH HAS RECENTLY BEEN USED FOR PRODUCTION OF THE SO-CALLED SECOND-GENERATION BIOFUELS.PRETREATMENT PROCESS IS AN ESSENTIAL STAGE TO IMPROVE THE DIGESTIBILITY OF LIGNOCELLULOSIC SUBSTRATES. IN THIS PAPER, AN ORGANOSOLV PROCESS WAS USED TO IMPROVE THE METHANE YIELD BY SOLID-STATE ANAEROBIC DIGESTION (SSAD) OF THREE LIGNOCELLULOSIC SUBSTRATES (ELM, PINE WOOD, AND RICE STRAW). TO OUR KNOWLEDGE, THERE IS NO PUBLICATION ON USING ORGANOSOLV PRETREATMENT PRIOR TO SSAD. THE UNIQUE ADVANTAGE OF THE ORGANOSOLV PRETREATMENT IS TO SEPARATE LIGNIN AS A VALUE ADDED BY PRODUCT. THE ORGANOSOLV PRETREATMENT WAS CONDUCTED IN FOUR DIFFERENT CONDITIONS (AT 150 AND 180OC FOR 30 AND 60 MIN) USING 75% ETHANOL SOLUTION ON THE LIGNOCELLULOSIC MATERIALS AND THE METHANE PRODUCTION YIELD THROUGH THE SSAD WAS INVESTIGATED. THE RESULTS SHOWED THAT THE TOTAL METHANE YIELD OF THE PRETREATED ELM, PINE, AND RICE STRAW WAS ENHANCED BY 90, 83, AND 36%, RESPECTIVELY. THE EFFECTS OF THE PRETREATMENT TEMPERATURE AND TIME ON METHANE YIELD WERE ALSO INVESTIGATED. STATISTICAL ANALYSES SHOWED THAT THE PRETREATMENT TEMPERATURE WAS THE MOST INFLUENCING FACTOR IN THE SSAD, WHILE THE EFFECT OF PRETREATMENT TIME ON METHANE PRODUCTION FROM ELM, PINE, AND RICE STRAW WAS NOT SIGNIFICANT. ALMOST ALL OF THE SUBSTRATES PRODUCED BIOGASES WITH METHANE CONTENTS BETWEEN 40% AND 50% BETWEEN DAY 14 AND DAY 55.

WIND SPEED PREDICTION IS CRITICAL FOR WIND ENERGY CONVERSION SYSTEMS SINCE IT GREATLY INFLUENCES THE ISSUES SUCH AS THE SCHEDULING OF A POWER SYSTEM, AND THE DYNAMIC CONTROL OF THE WIND TURBINE.IN THIS PAPER THE SHORT TERM WIND SPEED FORECASTING IN THE REGION OF ISTANBUL, TURKEY, APPLYINGTHE TECHNIQUE OF ARTIFICIAL NEURAL NETWORK (ANN) BASED ON LEVENBERG-MARQUARDT OPTIMIZATION METHODTO THE 10 MINUTE INTERVAL TIME SERIES REPRESENTATIVE OF THE SITE ISPRESENTED AND ALSO WE PRESENT A COMPREHENSIVE COMPARISON STUDY ON THE APPLICATION OF DIFFERENT ARTIFICIAL NEURAL NETWORKS ARCHITECTURESIN WIND SPEED FORECASTING AND COMPARE TRIANGULAR REDUCTION METHOD DATA WITH HECHT-NIELSEN APPROXIMATION METHOD DATA.THE DEVELOPED MODEL FOR SHORT TERM WIND SPEED FORECASTING SHOWED A VERY GOOD ACCURACY AND AGREEMENT BETWEEN PREDICTED AND REAL DATA.THE RESULTS ARE VALIDATED AND THE EFFECTIVENESS OF THE TRIANGULAR REDUCTION METHOD ANDHECHT-NIELSEN APPROXIMATION METHOD IS DEMONSTRATED.