Systematic analysis and multi-objective optimization of an integrated power and freshwater production cycle

This study represents the results of the analysis and optimization of an integrated system for cogenerating electricity and freshwater. This setup consists of a Solid Oxide Fuel cell (SOFC) for producing electricity. Unburned fuel of the SOFC is burned in the afterburner to increase the temperature of the SOFC's outlet gasses and operate a Gas turbine (GT) to produce additional power and operate the air compressor. At the bottom of this cycle, a combined setup of a Multi-Effect Desalination (MED) and Reverse Osmosis (RO) is considered to produce freshwater from the unused heat capacity of the GT's exhaust gasses. Also, a Stirling engine is used in the fuel supply line to increase the fuel's temperature. Using LNG and the Stirling engine will replace the fuel compressor with a pump which increases the system performance and eliminates the need for the expansion valve. To study the system performance a mathematical model is developed in Engineering Equation Solver (EES) program. Then, the system's simulated data from the EES has been sent to MATLAB to promote the best operating condition based on the optimization criteria. An energetic, exergetic, economic, and environmental analysis has been performed and a Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to achieve the goal. The two-objective optimization is performed to maximize the exergetic efficiency of the proposed system while minimizing the system's total cost of production. This cost is a weighted distribution of the Levelized Cost of Electricity (LCOE) and Levelized Cost of freshwater (LCOW). The results showed that the exergetic and energetic efficiencies of the system can reach 73.5% and 69.06% at the optimum point. The total electricity production of the system is 99 MW. The production cost is 11.71 Cents/kWh, of which 1.04 Cents/kWh is emission-related and environmental taxes. The freshwater production rate is 42.44 kg/s which costs 4.38 USD/m³.     

Microenvironment-Sensitive Fluorescent Ligand Binds Ascaris Telomere Antiparallel G-Quadruplex DNA with Blue-Shift and Enhanced Emission

The development of small molecules that can selectively target G-quadruplex (G4) DNAs has drawn considerable attention due to their unique physiological and pathological functions. However, only a few molecules have been found to selectively bind a particular G4 DNA structure. We have developed a fluorescence ligand Q1 , a molecular scaffold with a carbazole–pyridine core bridged by a phenylboronic acid side chain, that acts as a selective ascaris telomere antiparallel G4 DNA ASC20 ligand with about 18 nm blue-shifted and enhanced fluorescence intensity. Photophysical properties revealed that Q1 was sensitive to the microenvironment and gave the best selectivity to ASC20 with an equilibrium binding constant K_a=6.04×10⁵ M⁻¹. Time-resolved fluorescence studies also demonstrated that Q1 showed a longer fluorescence lifetime in the presence of ASC20. The binding characteristics of Q1 with ASC20 were shown in detail in a fluorescent intercalator displacement (FID) assay, a 2-Ap titration experiment and by molecular docking. Ligand Q1 could adopt an appropriate pose at terminal G-quartets of ASC20 through multiple interactions including π–π stacking between aromatic rings; this led to strong fluorescence enhancement. In addition, a co-staining image showed that Q1 is mainly distributed in the cytoplasm. Accordingly, this work provides insights for the development of ligands that selectively targeting a specific G4 DNA structure.     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

Genetic algorithm to simultaneously optimise stope sequencing and equipment dispatching in underground short-term mine planning under time uncertainty

ABSTRACT

The main issue in short-term planning optimisation for underground mining is organising the mining process with limited resources in the form of equipment and materials to satisfy production targets and stable feed grade requirements. In this paper, an integrated optimisation model is proposed based on an individual generation algorithm and an improved Genetic Algorithm to simultaneously optimise stope extraction sequencing and timing, extracted ore grade and equipment dispatching. The model objectives are to shorten the time gap between the stope mining processes and the overall working time. When the uncertainty of equipment working time is taken into account in a short-term scheduling model, the Monte Carlo simulation is applied to evaluate the risk of not meeting the production target. A modification strategy is defined to evaluate equipment failure. Consequently, any available equipment is automatically reassigned to the mining site to replace the broken-down equipment. A case study is used to validate the model in the Sanshandao gold mine of China to formulate an optimal monthly schedule. Compared with the conventional approach, the new model could reduce the variance of ore tonnage and feed grade and improve the equipment allocation efficiency. Discussions are presented to address the uncertainty.     

Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions

The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of food grain commodity shipments while considering procurement uncertainty, greenhouse gas emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimisation model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimisation approach considers minimisation of total relative regret associated with total cost subject to several real-time constraints. A version of Particle Swarm Optimisation with Differential Evolution is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.     

Individual-based multi-objective optimal structured treatment interruption for HIV infection

In this paper, based on the measurable quantities from an individual patient that has infection to human immunodeficiency virus (HIV) and his/her condition is near to acquired immune deficiency syndrome (AIDS), individual-based multi-objective optimal treatments have been proposed. Firstly, the most effective parameters of the patient in computing Long-term non-progressor (LTNP) equilibrium are derived using global sensitivity analysis (GSA). To accomplish GSA effectively, Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) are utilized to rank each of the parameters based on each state of the 5-dimensional model. Then, these results are used by Dempster–Shafer (D–S) evidence theory (DSET) to rank the most effective parameters comprehensively. Now, these effective identified parameters are estimated using extended Kalman filter (EKF), which its covariance matrices are optimized based on particle swarm optimization (PSO) algorithm. Thus, the proposed methodology gives a calibrated model corresponding to the individual patient. Based on this calibrated model, the LTNP equilibrium related to the individual patient is derived. Using the derived individual-based LTNP equilibrium optimal structured treatment interruption (STI) strategies are extracted by defining suitable multi-objective optimization problem and solving it through using non-dominated sorting genetic algorithm-II (NSGA-II). The results demonstrate that the proposed optimal treatments are able to effectively reach LTNP equilibrium with using the minimum and maximum drug usage of 3.6% and 35.1% of full drug usage treatment. Meanwhile, the different optimal treatments give the decision-makers enough flexibility to choose the suitable treatment based on existing facilities and necessities.     

Ab initio calculations of the NO2 fission for CL-20 conformers

NO₂ fission is regarded to be the most important initial decomposition process of 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20). In this study, four CL-20 conformers based on the ε-CL-20 were obtained after the optimization at m062x/cc-pvtz level, and the bond length, bond order and bond dissociation energy of the N-N bonds were examined to investigate the stability of these bonds. In addition, the rate constants and activation energy of the NO₂ fission were evaluated using the microcanonical variational transition state theory (μVT). The calculation results have shown that N-N bonds in the case of pseudo-equatorial and axial of nitro groups are the most stable and the least stable, respectively, by evaluating the bond length, bond order and minimum energy path (MEP). The NO₂ fission rate constants are affected by not only the stability of N-N bonds but also the repulsion forces from the other nitro groups, and the fission process for pseudo-equatorial positioning of nitro groups is easier to be accelerated due to the increase of the repulsion forces. The decomposition of CL-20 conformer may mainly originate from the fission of the pseudo-equatorial positioning of nitro groups, especially for CL-20 III conformer because of the significant low activation energy.     

Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.     

An improved particle swarm optimisation for unit commitment in microgrids with battery energy storage systems considering battery degradation and uncertainties

Using electric storage systems (ESSs) is known as a viable strategy to mitigate the volatility and intermittency of renewable distributed generators (DGs) in microgrids (MGs). Among different electric storage technologies, battery energy storage (BES) is considered as the best option. In unit commitment (UC) module, the set of committed dispatchable DGs along with their power, power exported to/imported from macrogrid and status and power of ESS units are determined. In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES‐integrated MGs, while the uncertainties of demand, renewable generation and market price are considered and dealt with robust optimisation. UC is formulated as a multi‐objective optimisation problem whose objectives are MG operation cost and BES degradation. The resultant multi‐objective optimisation problem is converted into a single‐objective optimisation problem and the effect of weight factors on MG operation cost and BES lifecycle are investigated. The results show that by consideration of BES degradation in objective function, BES lifecycle increases from 350 to 500 and the minimum depth of charge increases from 5.5% to 34%; however, MG operation cost increases from $8717 to $8910.2. The results also show that by consideration of uncertainties, MG's operation cost increases by 8.22%.     

高压下CL-20拉曼光谱和红外光谱相变研究

为了更好地认识和了解CL-20晶体结构演变规律和相变行为,利用金刚石对顶砧超高压实验技术,在0~50GPa下,研究了高压下ε-CL-20的原位拉曼光谱和红外光谱。结果表明,CL-20晶体在整个加压过程中存在两个相变,第一个相变发生在4.2~7.5GPa,认为是ε相到对称性更低的γ相转变,相变产生的原因是在压强的作用下,笼环外的硝基方向发生改变,电子云密度重置导致的分子构型转变;第二个相变发生在14.2~18.9GPa,属于γ相到ζ相的晶体结构转变;卸压后,拉曼和红外光谱恢复常压状态,表明CL-20晶体在研究压强范围内的相变过程是可逆的。