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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.

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.

Publisher Copyright: © 2022 IEEE. In the Nordic power system, the frequency of the system oscillates constantly around the nominal 50 Hz value with a period of around 40 to 90 s, i.e., in the frequency range around 0.011-0.025 Hz. This ultra low frequency oscillation (ULFO) deteriorates the frequency quality in the entire system, causes unnecessary control actions and may cause wear and tear of turbines. In the worst case, the oscillation may endanger frequency stability of the system. This paper analyzes the properties of this oscillations during a period of seven years using an ambient modal identification method (mode meter). The paper shows that the ULFOs can be analyzed using ambient modal identification methods and a similar approach is recommended to be used for analyzing ULFOs in other power systems as well. Furthermore, the paper gives insight into the long term characteristics of the ULFOs in the Nordic power system and the results indicate that for example the amplitude of the ULFOs has an increasing trend during the analysis period (and their damping has a decreasing trend). These findings indicate a need for monitoring the ULFOs and develop actions for mitigating them. Peer reviewed

The current crude phenol separation process is featured by the conventional distillation columns and its close boiling range between the components significantly increases the energy consumption. Compared to the conventional heat integrated technology, the advanced heat integrated technology has more or less advantage of simple structure, higher energy efficiency and economic benefit in some specific separation schemes but is lack of the application in process transformation. It has the potential to be a high-energy-efficient way to improve the energy efficiency of the crude phenol separation process. An improved middle vapor recompression distillation column (IMVRC) was proposed on the basis of the conventional middle vapor recompression distillation column (MVRC), with more consideration of operating pressure set and separated stage. The vapor recompression distillation column (VRC) and MVRC are used as the competitive configurations to estimate the performance of IMVRC for separating three different boiling range mixtures. Besides, the crude phenol process is established as two steam-driven and three electrical-driven processes with the conventional and advanced heat integrated technologies used. Furthermore, the flexibility of IMVRC for the process design is investigated and the extended structure of IMVRC (E-IMVRC) for the crude phenol separation process is achieved. The results show the IMVRC has the preferable energy and total annual cost (TAC) benefits in all the different boiling range mixtures. And the different separated stage makes the distinct structure of IMVRC with different cooling and heating duty arrangement, which benefits the potential heat integrated flexibility in the process design. Consequently, the electrical-driven processes have the significant TAC saving than the steam-driven processes. Moreover, the E-IMVRC performs best in all the 4E analysis.

Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

AbstractWith the increasing proportion of natural gas in power generation, natural gas network and electricity network are closely coupled. Therefore, planning of any individual system regardless of such interdependence will increase the total cost of the whole combined systems. Therefore, a multi-objective optimization model for the combined gas and electricity network planning is presented in this work. To be specific, the objectives of the proposed model are to minimize both investment cost and production cost of the combined system while taking into account the N−1 network security criterion. Moreover, the stochastic nature of wind power generation is addressed in the proposed model. Consequently, it leads to a mixed integer non-linear, multi-objective, stochastic programming problem. To solve this complex model, the Elitist Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to capture the optimal Pareto front, wherein the Primal–Dual Interior-Point (PDIP) method combined with the point-estimate method is adopted to evaluate the objective functions. In addition, decision makers can use a fuzzy decision making approach based on their preference to select the final optimal solution from the optimal Pareto front. The effectiveness of the proposed model and method are validated on a modified IEEE 24-bus electricity network integrated with a 15-node natural gas system as well as a real-world system of Hainan province.