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Torrefaction is considered as a method for producing biofuels with improved characteristics compared to those of the “raw” biomass (higher calorific value, moisture resistance, better grindability). The torrefaction process is an endothermic process that is usually carried out in a gaseous atmosphere in the absence of oxygen. To reduce the required heat input, it is proposed to employ the oxidative torrefaction and conduct the process in a fluidized bed agitated with flue gases containing less than 6% oxygen. Preliminary studies of the oxidative torrefaction of sunflower husks, including thermogravimetric analysis of the treated material, have shown that the heat treatment time for the biomass should be at least 5 min. A fluidized bed is a reactor with ideal mixing of the treated material where uniform treatment of raw material particles cannot generally be attained. To overcome this disadvantage of the fluidization technique and achieve the required residence time for biomass in a fluidized bed during a continuous torrefaction process, it was proposed to equip a torrefaction reactor with a series of vertical baffles spaced at 50 mm. These baffles induce a loop-like flow of the processed biomass from the inlet to the outlet of the reactor. To investigate the residence time for husk particles in the reactor, a tracer, which was colored to husk particles' color with a water-soluble dye which did not change the weight and size of the particles, was injected into the bed of uncolored particles. Tracer samples were taken every 30 s at the outlet of the reactor and were analyzed using a special procedure to determine the fraction of colored particles in each sample. This enabled us to gauge the time during which the colored particles injected into the fluidized bed reached the point of their discharge from the bed. Studies performed in a “cold” model of the reactor showed that a series of vertical baffles in the bed can provide the required residence time for biomass in a reactor including commercial reactors. Plates can provide the necessary biomass residence time in the reactor.

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

Abstract An ideal tracker which follows the sun both north-south and east-west is designed and constructed. It is suitable for mounting a linear concentrator, focusing parabolic concentrator or photovoltaic array of aperture area 2 m2. The apparatus is built with two axes equational mount and controlled by a microprocessor system. The tracking is carried out by utilizing the astronomical coordinates of the sun. As such the present system gives improved tracking capability over the conventional sensor controlled tracker systems. The performance of the tracking system was studied with photovoltaic array mounted on it and tracking error of less than one degree was achieved. This tracking error can be reduced further by proper software.

Abstract In many visions and roadmaps, there is a broad agreement that fuel cells – both for stationary and mobile applications – are the key technology to allow the development of a hydrogen infrastructure. Furthermore, this development is generally thought to be based on a gradual, decentralised evolution. Nevertheless, in this paper it is argued that, taking into account the entire hydrogen chain (production, transport, storage, distribution and end-use), this decentralised fuel-cell based philosophy shows some serious flaws. Therefore, a new hydrogen-transition approach was pushed forward: mixing in of hydrogen into the natural-gas bulk. Using Flanders – the Northern part of Belgium – as a case study, the development of a transitory hydrogen infrastructure has been studied, taking into account the entire hydrogen chain and its dynamics, from production to end use. In a next step, this transition is being quantified. An optimisation model has been developed using Matlab and the commercial solvers GAMS and CPLEX. Following a mixed-integer linear-programming approach, this model is able to determine the economically optimal hydrogen-production mix and operational behaviour of each hydrogen-production plant separately. The model then allows gaining valuable insights in the importance of storage and the influence of fuel prices and carbon taxes with regard to the development of an early hydrogen economy.

Abstract In the new competitive electricity supply industry, there is a renewed interest in algorithms that can provide savings in operation costs. An optimal scheduling of generators can provide substantial annual savings in fuel costs, but this highly constrained non-linear mixed integer optimisation problem can only be full v solved by complete enumeration, a process which is not computationally feasible for realistic power systems. In the recent past, evolutionary computation techniques have been applied to the solution of the unit commitment problem, but when implemented as stand alone systems, they suff er from computational time limitations, especially when the systems are scaled up. This paper proposes a hybridgenetic algorithm incorporating a priority list unit ordering scheme to solve the generator scheduling problem. Test results on networks with up to 110 generators are presented and the results demonstrate the viability of the hybrid GA method for unit commitment in realistic power systems.

This paper gives a introduction of a SMES unit using 2G HTS wires. A complete SMES system including both superconducting coils and control circuit has been designed to operate at 77 K. Three single-phase H-bridge converters have been used in the control circuit. A loop control signal is sent out by using 32 fixed point Digital Signal Processor (DSP). The complete circuit has been both modelled in simulation and built experimentally. The results validate that this SMES successfully compensates a voltage sag in a power system.