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The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.

Flare stacks are a key element in the safety of petrochemical and refinery industries; the correct operating conditions thereof must be ensured through a schedule for the periodic reviewing of all parts thereof. Advances in Non-Destructive Testing (NDT) in recent years and the application thereof to this field allow reliable, objective information to be obtained. This article describes and groups together the main applicable NDT techniques, analysing their advantages and disadvantages, updated with the new possibilities offered by unmanned aircraft or drones.

Flare stacks are a key element in the safety of petrochemical and refinery industries; the correct operating conditions thereof must be ensured through a schedule for the periodic reviewing of all parts thereof. Advances in Non-Destructive Testing (NDT) in recent years and the application thereof to this field allow reliable, objective information to be obtained. This article describes and groups together the main applicable NDT techniques, analysing their advantages and disadvantages, updated with the new possibilities offered by unmanned aircraft or drones.

Abstract Emissions and efficiency of a pellet boiler (40 kW) at nominal load were compared with emissions and efficiency at reduced load, while fired with six biomass pellets. The pellets include reed canary grass ( Phalaris arundinacea ), pectin waste from citrus shells ( Citrus reticulata ), sunflower husk ( Helianthus annuus ), peat, wheat straw ( Triticum aestivum ) and wood pellets. The measurements of emissions comprised of carbon monoxide (CO), nitrogen oxides (NO x ), sulphur oxides (SO x ) and flue dust mass concentrations (using DIN plus and isokinetic sampling techniques). Emissions varied as a function of operational loads, for each type of pellets. The CO emissions were insignificant with reed canary grass (RCG), citrus pectin waste (CPW) and straw pellets at nominal load, however, at reduced load same pellets emitted 1.9, 4.0 and 7.4 times higher CO than wood pellets, respectively. Peat pellets emitted maximum CO at nominal load (4221.1 mgNm −3 , 12.6 times higher than wood pellets) however; at reduced load CO emission was insignificant. The highest NO x emissions were reported with CPW, which were 3.4 and 4.6 times higher than wood pellets at nominal load and reduced load, respectively. Dust emissions were highest with sunflower husk and lowest with RCG pellets, at both operational modes. The best performance was reported with wood pellets, followed by RCG and pectin pellets, however, wood pellets combustion emitted 1.7 and 2.0 times higher dust DIN plus than RCG at nominal and reduced loads, respectively. Not only fuel specific combustion optimization but also operational load specific optimization is essential for efficient use of agro-pellets in this type of boilers.

Abstract Emissions and efficiency of a pellet boiler (40 kW) at nominal load were compared with emissions and efficiency at reduced load, while fired with six biomass pellets. The pellets include reed canary grass ( Phalaris arundinacea ), pectin waste from citrus shells ( Citrus reticulata ), sunflower husk ( Helianthus annuus ), peat, wheat straw ( Triticum aestivum ) and wood pellets. The measurements of emissions comprised of carbon monoxide (CO), nitrogen oxides (NO x ), sulphur oxides (SO x ) and flue dust mass concentrations (using DIN plus and isokinetic sampling techniques). Emissions varied as a function of operational loads, for each type of pellets. The CO emissions were insignificant with reed canary grass (RCG), citrus pectin waste (CPW) and straw pellets at nominal load, however, at reduced load same pellets emitted 1.9, 4.0 and 7.4 times higher CO than wood pellets, respectively. Peat pellets emitted maximum CO at nominal load (4221.1 mgNm −3 , 12.6 times higher than wood pellets) however; at reduced load CO emission was insignificant. The highest NO x emissions were reported with CPW, which were 3.4 and 4.6 times higher than wood pellets at nominal load and reduced load, respectively. Dust emissions were highest with sunflower husk and lowest with RCG pellets, at both operational modes. The best performance was reported with wood pellets, followed by RCG and pectin pellets, however, wood pellets combustion emitted 1.7 and 2.0 times higher dust DIN plus than RCG at nominal and reduced loads, respectively. Not only fuel specific combustion optimization but also operational load specific optimization is essential for efficient use of agro-pellets in this type of boilers.

In the current situation with the unprecedented deployment of clean technologies for electricity generation, it is natural to expect that storage will play an important role in electricity networks. This paper provides a qualitative methodology to select the appropriate technology or mix of technologies for different applications. The multiple comparisons according to different characteristics distinguish this paper from others about energy storage systems. Firstly, the different technologies available for energy storage, as discussed in the literature, are described and compared. The characteristics of the technologies are explained, including their current availability. In order to gain a better perspective, availability is cross-compared with maturity level. Moreover, information such as ratings, energy density, durability and costs is provided in table and graphic format for a straightforward comparison. Additionally, the different electric grid applications of energy storage technologies are described and categorised. For each of the categories, we describe the available technologies, both mature and potential. Finally, methods for connecting storage technologies are discussed.

In the current situation with the unprecedented deployment of clean technologies for electricity generation, it is natural to expect that storage will play an important role in electricity networks. This paper provides a qualitative methodology to select the appropriate technology or mix of technologies for different applications. The multiple comparisons according to different characteristics distinguish this paper from others about energy storage systems. Firstly, the different technologies available for energy storage, as discussed in the literature, are described and compared. The characteristics of the technologies are explained, including their current availability. In order to gain a better perspective, availability is cross-compared with maturity level. Moreover, information such as ratings, energy density, durability and costs is provided in table and graphic format for a straightforward comparison. Additionally, the different electric grid applications of energy storage technologies are described and categorised. For each of the categories, we describe the available technologies, both mature and potential. Finally, methods for connecting storage technologies are discussed.