Life cycle activity analysis: logistics and environmental policies for bottled water in Portugal

An innovative mathematical programming decision support model –Life Cycle Activity Analysis (LCAA)– is presented, integrating considerations of optimal allocations of resources and impacts upon the environment during the life cycle of products. LCAA is based on the classical formulation of activity analysis and on the life cycle assessment framework. The concept of linear activities is extended to embrace mass and energy fluxes over the entire life cycle of products including their environmental impacts. Special attention is given to the presence of loops in the product chains, such as those occurring when materials/products are recovered (reused, recycled.). An application brought from the Portuguese bottled water industry is described. The model features alternative activities for production technologies and product recovery strategies and permits the joint consideration of monetary costs and environmental burdens. The results obtained under five scenarios, including distinct disposal strategies and environmental constraints, are discussed.     

Integration of polynomials over n-dimensional polyhedra

Explicit formulae and algorithms for computing integrals of polynomials over n-dimensional polyhedra are given. Two different approaches are discussed: one uses a decompositive representation, while the other one uses a boundary representation of the polyhedron. The algorithms are followed by a discussion of the complexity. In the appendix the pseudo-code and some examples of calculation are given.     

Electric load analysis using an artificial neural network

Load forecasting in the current, increasingly liberalized, electricity power market is of crucial importance as a means for producers to optimize and rationalize energy supply. A number of electric power companies are equipped to make forecasts with the aid of traditional statistical methods. This paper presents the use of an artificial neural net to an hourly based load forecasting application for a small electric grid on an Italian island (Lipari) not connected to the mainland. The aim was to examine the forecasting ability of a neural net in a situation where the electric load was subject to considerable seasonal variations over the year. The variations are affected by energy demand related to the tourism season as well as by climatic conditions, especially temperature. The network developed was a multi‐layer perceptron type built on three layers trained with a back‐propagation algorithm. The input layer receives all the most relevant information regarding: the class of day type, the hour in the daytime, the load and background temperature recorded at the indicated time for the months of March, August and October whilst the output layer provides the forecast value at the indicated time in December. The results obtained are encouraging; in the training phase the RMS error rate was around 2% and this rate settled at about 2.6% during testing. As both the margins of error recorded are acceptable, the use of a neural network for electric load forecasting applications can be considered an attractive option. Copyright © 2005 John Wiley & Sons, Ltd.     

Use of homogenisation pressure to improve quality and functionality of probiotic fermented milks containing Lactobacillus rhamnosus BFE 5264

The effects of 60‐MPa milk homogenisation treatment were investigated on the viability of the probiotic strain Lactobacillus rhamnosus BFE5264, when used as yoghurt co‐starter, as well as on the aroma profile, texture and microstructure of the resulting set‐type fermented milks. The results demonstrated that the combined use of homogenisation pressure and co‐inoculation of the probiotic strain with the traditional yoghurt starters allowed the reduction in the product coagulation time, the increase of the probiotic strain viability and the improvement of the product volatile molecule profiles. The rheological indices and the microstructure results indicated the positive effects of the milk homogenisation treatment on the product viscosity index and exopolysaccharide production.     

Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems

The energy produced by thermal solar plants does not have to be limited solely to hours of sunlight. It is possible to conceive a storage system and it is possible to extend the production of heat beyond the hours of full sunshine.     

Advanced analytics for detection and diagnosis of false alarms and faults: A real case study

Onshore and offshore wind farms require a high level of advanced maintenance. Supervisory control and data acquisition (SCADA) and condition monitoring systems are now being employed, generating large amounts of data. They require robust and flexible approaches to convert dataset into useful information. This paper presents a novel approach based on the correlations of SCADA variables to detect and identify faults and false alarms in wind turbines. A correlation matrix between all the SCADA variables is used for pattern recognition. A new method based on curve fittings is employed for detecting false alarms and abnormal behaviours or faults in the components. The study is done in a real case study, validated with false alarms.     

Study of the antioxidant properties of extracts obtained from nopal cactus (Opuntia ficus-indica) cladodes after convective drying

BACKGROUND: The process of convective drying was evaluated in terms of the bioactive compounds contained in nopal samples before and after dehydration. Total polyphenol, flavonoid, flavonol, carotene and ascorbic acid contents were determined in undehydrated and dehydrated samples. Two drying temperatures (45 and 65 °C) and two air flow rates (3 and 5 m s^?1) were evaluated. The rheology of samples under the best drying conditions was also studied, since it provides important information regarding processing (mixing, flow processing) as well as the sensory attributes (texture) of rehydrated samples. RESULTS: Non‐Newtonian shear‐thinning behaviour was observed for samples dried at 45 °C, while samples dried at 65 °C showed shear‐thickening behaviour, possibly caused by thermal chain scission of high‐molecular‐weight components. CONCLUSION: The best conditions for bioactive compound preservation were a drying temperature of 45 °C and an air flow rate of 3 m s^?1, resulting in 40.97 g phenols, 23.41 g flavonoids, 0.543 g β‐carotene and 0.2815 g ascorbic acid kg^?1 sample as shown in table 3. Copyright © 2011 Society of Chemical Industry     

Comparison of conventional and spherical reactor for the industrial auto-thermal reforming of methane to maximize synthesis gas and minimize CO2

Auto-thermal reforming (ATR), a combination of exothermic partial oxidation and endothermic steam reforming of methane, is an important process to produce syngas for petrochemical industries. In a commercial ATR unit, tubular fixed bed reactors are typically used. Pressure drop across the tube, high manufacturing costs, and low production capacity are some disadvantages of these reactors. The main propose of this study is to offer an optimized radial flow, spherical packed bed reactor as a promising alternative for overcoming the drawbacks of conventional tubular reactors. In the current research, a one dimensional pseudo-homogeneous model based on mass, energy, and momentum balances is applied to simulate the performance of packed-bed reactors for the production of syngas in both tubular and spherical reactors. In the optimization section, the proposed work explores optimal values of various decision variables that simultaneously maximize outlet molar flow rate of H₂, CO and minimize molar flow rate of CO₂ from novel spherical reactor. The multi-objective model is transformed to a single objective optimization problem by weighted sum method and the single optimum point is found by using genetic algorithm. The optimization results show that the pressure drop in the spherical reactor is negligible in comparison to that of the conventional tubular reactor. Therefore, it is inferred that the spherical reactor can operate with much higher feed flow rate, more catalyst loading, and smaller catalyst particles.