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Economic load dispatch (ELD) performs an important part in the economic operation of power system. The ELD problem is considered as a non-linear constrained optimisation problem. The problem becomes non-convex and non-smooth when the generators’ prohibited zones and valve-point effect are considered. The purpose of this work is to present a solution strategy to solve ELD problem in an efficient way while considering several aspects of ELD. The strategy employs a hybrid mechanism involving a quantum mechanics inspired particle swarm optimisation (PSO). The conventional PSO is modified by integrating quantum mechanics theory that redefines the particles positions and velocities in more diverse manner and therefore explores more search space. The PSO is further upgraded from a single population-based to a multi-population one. Such feature of the method delivers a fine balance between the local and global searching abilities. The simulations are carried by considering several cases of thermal units by varying different combinations of system configurations such as with/without valve-point effect, with/without network loss and for one or several hours of load demand. The results are quite promising and effective compared with several benchmark methods.

Nitrocellulose is a cellulose derivative that has many potential applications. Nitrocellulose can bemade through nitration reactions by reacting cellulose and nitric acid at low temperatures. Cellulose can be obtained from lignocellulose biomass such as palm oil empty fruit bunches (POEFBs). In this study, techno-economic evaluation of nitrocellulose production from POEFBs was investigated with various types of alkaline and acid pretreatments. Pretreatment of POEFBs with alkaline and acid was used to purify cellulose fraction as raw material for nitrocellulose. The combination process of POEFBs pretreatment with alkaline and acid can be classified into 4 process routes such as ammonium hydroxide and sulfuric acid pretreatment (Route-1), ammonium hydroxide and acetic acid pretreatment (Route-2), sodium hydroxide and sulfuric acid pretreatment (Route-3), and sodium hydroxide and acetic acid pretreatment (Route-4). The results showed that ammonium hydroxide and sulfuric acid pretreatment (Route-1) was the most profitable route to produce nitrocellulose. Economic parameter values such as return of investment (ROI), payback period (PBP), net present value (NPV) and internal rate of return (IRR) from ammonium hydroxide and sulfuric acid pretreatment (Route-1) were 11.49%, 5.85 years, US$ 442,427 and 13.35%.

Abstract The subject of energy saving in distillation column sequencing is of critical importance. Heat integration in a multicomponent separation can be industrialized by saving considerable energy and cost. In this work, external heat-integrated distillation column with external heat exchanger has been studied and the annual cost function has been optimized using Genetic Algorithm. Introducing the layout and binary matrices enabled the investigation of all possible locations for the heat exchanger arrangement successfully. Moreover, exchangers heat loads and compressors pressure, have also been considered as optimization variables. Benzene, toluene, xylene and n-alkanes, separations have been studied as case studies. It has been demonstrated that the proposed optimization method in the alkane separation case decreased the total annual cost by 22.6% in comparison with the proposed thermally coupled distillation sequence columns. The heat integration of external heat exchangers in an external heat-integrated distillation column configuration and the proposed dived-wall column resulted in decreasing the total annual cost by 17% and 39% respectively, in comparison with conventional distillation columns.

The short-circuit computations are usually performed in power system studies to determine currents and voltages in the power grid under fault. The interconnection of Distributed Generation (DG) increases the short-circuit currents and may cause that electric equipments overcome their capacity limits. At the same time, the increment of renewable energy sources and other forms of DG, introduces more uncertainties to the network. This issue has motivated the development of methodologies to evaluate the effects of DG on distribution networks. The fuzzy sets theory is implemented to evaluate the impact of uncertainties (of both power generation and network parameters) on fault levels considering the IEC Standard 60909. The solution is compared to Monte-Carlo Simulation (MCS) which is the most usual simulation used to solve stochastic problems.

A metafrontier slack-based efficiency measure is presented to measure environmental efficiency for various regions in China. The objective of the new approach is to investigate the change of environmental efficiency while incorporating group heterogeneities and all variable slack and environmental pollutants into environmental efficiency analysis. Global production technology is used to improve the discriminating power of environmental efficiency measurement. An empirical analysis of regional environmental efficiency is carried out incorporating sulfur dioxide emissions and the chemical oxygen demand (COD) of China’s regions from 2000–2011. Results indicate that excessive emissions pollution is the major cause of environmental inefficiency. Most of the regions return environmental efficiency values. Significant regional technology gaps in environmental efficiency are found between the east, central, and west areas. Finally, some policy implications are presented from the empirical results.

Aiming at the energy multivariate heterogeneity of thermal system and natural gas system, a novel optimization model of integrated energy system considering thermal inertia and gas inertia is proposed. First, the dynamic characteristics of the thermal system in the integrated energy system are studied, and the inertia model of the heat network pipeline and thermal building is established. Second, the characteristics of natural gas pipeline network storage are studied, and the natural gas storage and pressure energy generation models are established. Then, the optimization objective of the minimum integrated operating cost of the integrated energy system is established, and the YALMIP solver is used to solve it. Finally, a numerical example is introduced to analyze the operating cost of the integrated energy system in different scenarios, and it is verified that the integrated energy system optimization model considering the inertia of the thermal and gas proposed in this paper can effectively improve the system regulation capability and reduce the system operating cost.

Time series forecasting in the energy sector is important to power utilities for decision making to ensure the sustainability and quality of electricity supply, and the stability of the power grid. Unfortunately, the presence of certain exogenous factors such as weather conditions, electricity price complicate the task using linear regression models that are becoming unsuitable. The search for a robust predictor would be an invaluable asset for electricity companies. To overcome this difficulty, Artificial Intelligence differs from these prediction methods through the Machine Learning algorithms which have been performing over the last decades in predicting time series on several levels. This work proposes the deployment of three univariate Machine Learning models: Support Vector Regression, Multi-Layer Perceptron, and the Long Short-Term Memory Recurrent Neural Network to predict the electricity production of Benin Electricity Community. In order to validate the performance of these different methods, against the Autoregressive Integrated Mobile Average and Multiple Regression model, performance metrics were used. Overall, the results show that the Machine Learning models outperform the linear regression methods. Consequently, Machine Learning methods offer a perspective for short-term electric power generation forecasting of Benin Electricity Community sources.

Abstract Hydrothermal conversion coupled with high pressure water and steam of three de-oiled non-edible seed cakes of Jatropha curcasa, Pongammia pinnata and Tung to biocrude is explored in this work. The cakes were characterized for proximate, ultimate and ligno-cellulosic compositions using ASTM and TAPPI standard methods. All studies were conducted at high temperature and pressure in a semi-batch autoclave. Highest conversion with maximum biocrude yield (∼35.3%) was obtained with P. pinnata cake. Silica adsorption chromatography result showed higher oxygenated aromatics subfraction in both Tung biocrude and Jatropha biocrude. GC/MS qualitative analysis confirmed the presence of heavy, oxygenated/ nitrogenous and poly aromatic hydrocarbons with carbon number ranging from C 5 -C 47 in the biocrude. The viscosity, pH, density, total acidic number (TAN) and water content of biocrudes were evaluated using various standard methods. 34.2 to 45.8% increases in the calorific value is observed for biomass to biocrude. Biochar obtained were characterized using proximate and ultimate analysis and SEM, TGA techniques. Higher calorific values (24.7–26.3 MJ/kg) of all the biochar make them suitable for their potential application as briquettes in the furnace for power generation.