Extension of the anaerobic digestion model No. 1 (ADM1) to include phenolic compounds biodegradation processes for the simulation of anaerobic co-digestion of olive mill wastes at thermophilic temperature

This paper describes for the first time the extension of the anaerobic digestion model No. 1 (ADM1) to handle and simulate the anaerobic degradation processes of phenol compounds and homologues in olive mill wastewater (OMW) and olive mill solid waste (OMSW) at thermophilic temperature (55 degrees C). The general structure of the ADM1 was not changed except for the modifications related to the inclusion of phenolic compounds degradation processes into acetate and further into methane and CO(2). The effect of soluble phenolic compounds upon pH was taken into account in the pH simulation equations. The inhibitory effect of phenolic compounds on the fermenting process and methanogenic sub-populations was accounted for by the use of non-competitive inhibition functions. The most sensitive and new phenolic parameters were calibrated and validated using experimental data from our previous study dealing with the thermophilic anaerobic co-digestion of OMW with OMSW in semi-continuous tubular digesters. The simulation results indicated that the extended ADM1 was able to predict with reasonable accuracy effluent phenol concentrations and gas flow rates and effluent pH of various influent concentrations digested at hydraulic retention times (HRTs) of 36 and 24 days.     

Aqueous hydrazine borane N2H4BH3 and nickel-based catalyst: An effective couple for the release of hydrogen in near-ambient conditions

Nickel-based bimetallic catalysts were screened using the sodium borohydride NaBH₄ hydrolysis and the aqueous hydrazine borane N₂H₄BH₃ dehydrogenation. A total of 22 bimetallic catalysts were synthesized according to an easy process while focusing on metals like Fe, Co, Ni, Cu, Rh, Pd, Ag, Ir, Pt and Au. In the end, the bimetallic candidate Ni_87.5Pt_12.5 showed to be the most active and the most selective for the dehydrogenation of N₂H₄BH₃. At 70?°C, it is able to decompose N₂H₄BH₃ into 5.8 equivalents of H₂+N₂ in less than 12?min such as: N₂H₄BH₃?+?3H₂O?→?0.95 N₂?+?0.1 NH₃?+?B(OH)₃?+?4.85H₂. Durability and stability tests were also performed. In our conditions, Ni_87.5Pt_12.5 was found to suffer from small loss of performance because of an electronic evolution of the catalytic surface leading to modified sorption properties of the catalytic sites. Our main results are reported and discussed herein.     

Effects of cyclic torsional prestraining and overstrain on fatigue life and damage behavior of brass alloy

Many practical structural members and parts may be subjected to fluctuating plastic deformation by prestraining due to manufacturing and machining process (forming operation, straightening, etc.) and unintentional overstrains (misuse, accidents, under design, etc.). For this reason, the effect of the prestrain and periodic overstrains on fatigue life and damage behavior was being necessary considered for reasonable fatigue design. In this context, an experimental program was conducted to study the effects of overstrain and prestraining on fatigue life and damage behavior of brass alloy subjected to cyclic torsional loading. To establish baseline fatigue behavior, several virgin specimens were tested under fully-reversed strain control and constant amplitude fatigue torsional loading up to failure. The obtained experimental results showed that the fatigue life depends strongly on the strain amplitude and prestraining type (monotonic or cyclic). In addition, a beneficial effect in the fatigue life was observed for all tests with periodic overstrain. Cyclic fatigue fracture on a macroscopic scale revealed features reminiscent of locally ductile and brittle mechanisms. At the same time, microscopic analysis indicated a difference on fatigue fracture surface morphology between the conducted tests and those performed under constant amplitude loading.     

Minimization of Wireless Sensor Network Energy Consumption Through Optimal Modulation Scheme and Channel Coding Strategy

Wireless sensor networks (WSNs) are composed of sensor nodes generally powered by batteries, for which recharging or replacement is difficult. Since battery technology has not progressed as rapidly as semiconductor technology, energy efficiency has become increasingly important in WSN. On the other hand, data exchanged between nodes are vulnerable to corruption by errors induced by random noise, signal fading and other factors. Therefore, improving link reliability and reducing energy consumption are prime concerns in the design of wireless sensor networks. In this context, performing optimal modulation schemes with suitable channel coding process is a crucial task at the physical layer of this class of networks. This paper investigates the best modulation strategy to minimize the total energy consumption required to send a given number of bits. Energy consumption with both uncoded and coded modulation techniques including M-ary QAM (MQAM), M-ary PSK (MPSK), M-ary FSK (MFSK) and MSK is analytically analyzed and simulated over transmission time, modulation rate and transmission distance. A comparative analysis in terms of energy consumption and probability of Bit Error Rate (BER) referring to MSK modulation with proper Error Control Codes (ECC) approach is presented in this paper. We show that the gain achieved with coded MSK scheme is very promising for obtaining optimal energy network consumption.     

Slow-release fertilisers based on natural rubber

It is estimated that only between 30-70% of the fertiliser applied in agriculture is used for plant nutrition, the rest is carried away by ground water or lost through nitrification-denitrification. Apart from the economic loss due to wastage, severe environmental problems arise due to increasing nitrate concentrations in public drinking water. The research reports a method of binding the fertiliser urea into natural rubber thus forming a matrix of rubber encapsulated fertiliser. A special ‘split-feeding’ mixing technique is necessary for a good product; also found important were mixing and vulcanisation temperatures with the optimum being in the 70?82°C range and 95°C respectively.     

Prediction of burr height formation in blanking processes using neural network

Productivity and quality in sheet metal blanking processes part can be assessed by the burr height of the sheared edge after blanking. This paper combines predictive finite element approach with neural network modelling of the leading blanking parameters in order to predict the burr height of the parts for a variety of blanking conditions.Experiments on circular blanking operation has been performed to verify the validity of the proposed approach.The numerical results obtained by finite element computation including damage and fracture modelling and tool wear effects were utilized to train the developed simulation environment based on back propagation neural network modelling.A trained neural network system was used in predicting burr height of the blanked parts versus tool wear state and punch-die clearance.The comparative study between the results obtained by neural network computation and the experimental ones gives good results.     

Influence of physico-chemical treatment on the subsequent biological process treating paper industry wastewater

The present paper presents the main results of the biodegradation study of paper industry wastewater through physico-chemical treatment. Indeed, around 60% of chemical oxygen demand (COD) removal can be achieved by electroflocculation treatment. Furthermore, a removal efficiency of the COD of almost 91% has been obtained by biological treatment, with activated amount of sludge for 24 h of culture. Concerning the physico-chemical pre-treatment of the untreated, filtered and electroflocculated rejection effluents, it has been investigated through the degradation curve of COD studies.     

Adaptive control design using stability analysis and tracking errors dynamics for nonlinear square MIMO systems

This paper investigates adaptive control design for nonlinear square MIMO systems. The control scheme is based on recurrent neural networks emulator and controller with decoupled adaptive rates. Networks' parameters are updated according to an autonomous algorithm inspired from the Real Time Recurrent Learning (RTRL). The contributions of this paper are the determination of Lyapunov sufficient stability conditions for decoupled adaptive rates of the emulator and controller and the development of new adaptation strategies based on the tracking error dynamics and Lyapunov stability analysis to improve the closed loop performances. Efficiency of the proposed controller is illustrated with nonlinear system simulations. An application of the developed approaches to a hot-air blower is presented in order to validate simulations results.     

An algebra of product families

Experience from recent years has shown that it is often advantageous not to build a single product but rather a family of similar products that share at least one common functionality while having well-identified variabilities. Such product families are built from elementary features that reach from hardware parts to software artefacts such as requirements, architectural elements or patterns, components, middleware, or code. We use the well established mathematical structure of idempotent semirings as the basis for a product family algebra that allows a formal treatment of the above notions. A particular application of the algebra concerns the multi-view reconciliation problem that arises when complex systems are modelled. We use algebraic integration constraints linking features in one view to features in the same or a different view and show in several examples the suitability of this approach for a wide class of integration constraint formulations. Our approach is illustrated with a Haskell prototype implementation of one particular model of product family algebra.