A vapour compression chiller fault detection technique based on adaptative algorithms. Application to on-line refrigerant leakage detection

The existence of faults in vapour compression chillers plays a significant role in terms of energy efficiency loss, performance degradations, and even environmental implications. In this paper, a dynamic model-based fault detection technique suitable for real-time implementation is proposed. The main objective is to obtain a reliable and automated tool for fault detection in vapour compression chillers, which can be applied in steady-state or transient operation. The fault detection methodology is based on comparing actual and expected performance using an adaptative model and operating variables dynamic thresholds. The technique has been successful applied for on-line refrigerant leakage detection with experimental tests involving the artificial introduction of the fault in a laboratory vapour compression plant, showing the results its capability of detecting incipient leakage failure conditions avoiding false alarms.     

Performance investigation of a variable speed vapor compression system for fault detection and diagnosis

An experimental study has been performed to investigate the effect of four artificial faults on the performance of a variable speed vapor compression system. Experimental setup to test several artificial faults was made by modifying the conventional vapor compression test rig. Four major faults of compressor fault, condenser fault, evaporator fault, and refrigerant leakage, were implemented by observing the variation of cooling capacity. Two different rule-based modules for constant and variable speed operations were organized for an easy diagnosis of system faults. These two modules were applied differently as the cooling capacity satisfies the necessary air conditioning load. As a result, COP degradation due to the fault in a variable speed system is severer than that in a constant speed system.     

Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner

This paper presents a general methodology for developing a steady-state detector for a vapor compression system based on a moving window and using standard deviations of seven measurements selected as features. The feature thresholds and optimized moving window size were based upon steady-state no-fault tests and startup transient tests. The study showed that evaporator superheat and condenser subcooling were sufficient for determining the onset of steady-state during the startup transient. However, they misidentified steady-state during indoor temperature change tests where evaporator saturation temperature and air temperature change across the evaporator were needed for proper steady-state identification. Hence, the paper recommends including all fault detection and diagnosis (FDD) features in the steady-state detector to ensure the robustness of the detector because different features may play key roles with different transients.     

Detection of interferences in an additive manufacturing process: an experimental study integrating methods of feature selection and machine learning

Additive manufacturing becomes a more and more important technology for production, mainly driven by the ability to realise extremely complex structures using multiple materials but without assembly or excessive waste. Nevertheless, like any high-precision technology additive manufacturing responds to interferences during the manufacturing process. These interferences – like vibrations – might lead to deviations in product quality, becoming manifest for instance in a reduced lifetime of a product or application issues. This study targets the issue of detecting such interferences during a manufacturing process in an exemplary experimental setup. Collection of data using current sensor technology directly on a 3D-printer enables a quantitative detection of interferences. The evaluation provides insights into the effectiveness of the realised application-oriented setup, the effort required for equipping a manufacturing system with sensors, and the effort for acquisition and processing the data. These insights are of practical utility for organisations dealing with additive manufacturing: the chosen approach for detecting interferences shows promising results, reaching interference detection rates of up to 100% depending on the applied data processing configuration.     

嵌入式Linux在液压系统的状态监测与故障诊断中的应用

针对国产地下无轨采矿设备液压系统故障率高,而其检测和故障诊断手段落后的背景,本文提出了一种基于嵌入式Linux的多传感器信息的液压系统状态监测与故障诊断的设计方案,通过该系统能实时地、可靠地跟踪地下无轨采矿设备液压系统的工作状态,并能将设备运行状态发展趋势、故障信息通过友好的人机界面反映出来。     

Fuzzy goal-programming model with an artificial immune system (AIS) approach for a machine tool selection and operation allocation problem in a flexible manufacturing system

Some of the important planning problems that need realistic modelling and a quicker solution, especially in automated manufacturing systems, have recently assumed greater significance. In real-life industrial applications, the existing models considering deterministic situations fail as the true language adopted by foremen and technicians are fuzzy in nature. Thus, to map the situation on the shop floor to arrive at a real-time solution of this kind of tactical planning problem, it is essential to adopt fuzzy-based multi-objective goals so as to express the target desired by the management of business enterprises. This paper presents a fuzzy goal-programming approach to model the machine tool selection and operation allocation problem of flexible manufacturing systems. The model is optimized using an approach based on artificial immune systems and the results of the computational experiments are reported.     

An evaluation of machine learning and artificial intelligence models for predicting the flotation behavior of fine high-ash coal

In this study, five different machine learning (ML) and artificial intelligence (AI) models: random forest (RF), artificial neural networks (ANN), the adaptive neuro-fuzzy inference system (ANFIS), Mamdani fuzzy logic (MFL) and a hybrid neural fuzzy inference system (HyFIS) were employed to predict the flotation behavior of fine high ash coal in the presence of a novel “hybrid” ash depressant consisting of polyacrylamide chains grafted onto aluminium hydroxide nanoparticles: Al(OH)₃-PAM (Al-PAM). A total of 51 flotation tests were conducted on coal samples with 38% ash-content and a P₈₀ of approximately 49?μm. Different influencing variables of coal flotation including polymer dosage, pH, polymer conditioning time, sodium metasilicate dosage (commercial dispersant), and the impeller speed were used as inputs for the models. The combustible recovery and ash content of coal reported to the concentrate were used as response variables (outputs). For AI model development, 80% of the total data was used for training phase and 20% was used for testing phase. Coefficient of determination (R²) and root-mean-square error (RMSE) were used as performance indicators of the models. The MFL model showed the best accuracy for the prediction of the combustible recoveries and the froth ash contents for this specific feed. However, in case of any significant change in the characteristics of the feed, these models would have to be re-trained using the data obtained through further physical experimentation and/or process model simulations. Moreover as these models are trained on laboratory scale data, these are only good for the predictions at laboratory scale.     

Using genetic algorithms (GA) and a coloured timed Petri net (CTPN) for modelling the optimization-based schedule generator of a generic production scheduling system

The semiconductor manufacturing industry is one of the most complicated manufacturing systems in the world. Considering its complex problem nature, such as the unrelated parallel machine environment, dynamic job arrival, non-pre-emption, inseparable sequence-dependent set-up time, multiple-resource requirements, general precedence constraint, and job recirculation, this study proposed the optimization-based schedule generator (OptSG) for solving the generalized scheduling problems arising from the semiconductor manufacturing environment. The separation of the problem structure and problem configuration in OptSG contributes to the structural independence, making OptSG robust and convenient in analysis and problem-solving in real settings with changing properties. Meanwhile, an MILP model was proposed as a benchmark to estimate the validity of OptSG. Inseparable sequence-dependent set-up time and multiple-resource requirements that have not been addressed simultaneously in the literature were considered in this model. By using different evaluation criteria, including makespan, total completion time and maximum tardiness, experiments were conducted to compare the solutions of the MILP model, OptSG and dispatching rule-based heuristics (DRBH). The results validated the solution quality of OptSG.     

Genetic-Algorithm-Based Computational Models for Optimizing the Process Parameters of A-TIG Welding to Achieve Target Bead Geometry in Type 304 L(N) and 316 L(N) Stainless Steels

The weld-bead geometry in 304LN and 316LN stainless steels produced by A-TIG welding plays an important role in determining the mechanical properties of the weld and its quality. Its shape parameters such as bead width, depth of penetration, and reinforcement height are decided according to the A-TIG welding process parameters such as current, voltage, torch speed, and arc gap. Identification of a suitable combination of A-TIG process parameters to produce the desired weld-bead geometry required many experiments, and the experimental optimization of the A-TIG process was indeed time consuming and costly. Therefore it becomes necessary to develop a methodology for optimizing the A-TIG process parameters to achieve the target weld-bead geometry. In the present work, genetic algorithm (GA)-based computational models have been developed to determine the optimum/near optimum process parameters to achieve the target weld-bead geometry in 304LN and 316LN stainless steel welds produced by A-TIG welding.     

Graphene oxide-sulfated lanthanum oxy-carbonate nanocomposite as an adsorbent for the removal of malachite green from water samples with application of statistical optimization and machine learning computations

Sulfated lanthanum oxy-carbonate nanorods (S-La₂O₂(CO₃) NRs) was synthesized by the reverse micelle microemulsion method and then used to modify graphene oxide nanosheets to synthesize of graphene oxide-sulfated lanthanum oxy-carbonate (GO-S-La₂O₂(CO₃)) nanocomposite. By characterization of S-La₂O₂(CO₃) NRs and GO-S-La₂O₂(CO₃) nanocomposite by the Fourier Transform-Infrared (FT-IR) Spectrophotometry, Field Emission-Scanning Electron Microscopy (FE-SEM), Energy-dispersive X-ray spectroscopy (EDS), Transmission Electron Microscopy (TEM) and X-ray diffraction analysis (XRD), GO-S-La₂O₂(CO₃) was used for treatment of malachite green (MG). To find the optimum removal percentage (RP), influencing parameters were investigated by the response surface methodology based on central composite design (RSM-CCD). Adsorption mechanism was evaluated by Dubinin–Radushkevich (D-R), Langmuir, Temkin, Freundlich (two parameter equations) and Sips (Three parameter equations) isotherms and based on the results the adsorption of MG into the GO-S-La₂O₂(CO₃) nanocomposite obeyed by the Freundlich isotherm with the maximum adsorption capacity of 555.5 mg g^?1. Also, the results of kinetic analysis show that the adsorption of MG onto the GO-S-La₂O₂(CO₃) nanocomposite followed by the pseudo second order kinetic model. For estimation of adsorption behavior, different machine learning techniques are used and based on the results; ANFIS model has the confidential operation because of fuzzy procedure and flexibility of data mining in distributed adsorption data.     

介孔生物活性玻璃装载和释放抗癌药物表阿霉素的研究

将介孔58S生物活性玻璃(m58S)作为抗癌药物载体评价了其对表阿霉素的装载量和释放性能. 实验结果表明, m58S对亲水性药物表阿霉素的药物装载量为40%, 是普通溶胶-凝胶58S生物活性玻璃的3倍多, 并且具有更长效的缓释特性. 研究还发现释放介质的pH值对表阿霉素的释放速率有很大影响, pH值越低, 表阿霉素分子从载体材料中释放出的速率越快. 因此, 介孔生物活性玻璃是一种高效的药物缓释载体, 且药物释放速率受释放介质pH值影响, 有望成为药物控释型骨修复材料.     

Gold nanoparticle-based simple colorimetric and ultrasensitive dynamic light scattering assay for the selective detection of Pb(II) from paints, plastics, and water samples

Pb (II) is a common water pollutant with high toxicity. According to the CDC, about 310,000 U.S. children of ages 1-5 have high levels of lead in their blood that it is due to the exposure to lead from plastic toys and other products. As a result, the development of ultrasensitive assays for the real-time detection of Pb(II) from plastic toys and paints is very important for water controlling, clinical toxicology and industrial processes. Driven by the need to detect trace amounts of Pb(II) from water samples, we report a label-free, highly selective and ultra sensitive glutathione modified gold nanoparticle based dynamic light scattering (DLS) probe for Pb(II) recognition in 100 ppt level from aqueous solution with excellent discrimination against other heavy metals. The sensitivity of our assay to detect Pb(II) level in water is almost 2 orders of magnitude higher than the EPA standard limit. We have also demonstrated that our DLS assay is capable of measuring the amount of Pb(II) in paint, plastic toys, and water from MS river. A possible mechanism and operating principles of our DLS assay have been discussed. Ultimately, this nanotechnology driven assay could have enormous potential applications in rapid, on-site monitoring of Pb(II) from day-to-day sample.     

Design and synthesis of immunoconjugates and development of competition inhibition enzyme-linked immunosorbent assay (CIEIA) for the detection of O-isopropyl methylphosphonofluoridate (sarin): an organophosphorous toxicant

Three haptens of the organophosphorus (OP) toxicant ‘sarin’ having different spacer arm were designed and synthesized. Haptens were conjugated with BSA (bovine serum albumin) and ovalbumin (OVA) for raising antibody and coating antigen. High antibody titer with higher specificity was obtained from 4-(4-(isopropoxy(methyl)phosphoryloxy)phenylamino)-4-oxobutanoic acid (hapten B) having reasonable long spacer arm. For the standard curve, an IC₅₀ (inhibitory concentration) of free antigen was found to be 0.415 μg mL⁻¹ on the basis of indirect competitive ELISA. The study revealed that heterology in competition inhibition enzyme immunoassay (CIEIA) produced remarkable improvement in the sensitivity and specificity of the assay. Under the optimized conditions, the quantitative working range was found to be 0.19-1.56 μg mL⁻¹ with a limit of detection (LOD) of 0.05 μg mL⁻¹. The antibodies showed negligible cross reactivity (CR) with other OP toxicants and pesticides, which makes the assay suitable for the selective detection of sarin.     

Software design specification and analysis technique (SDSAT) for the development of safety-critical systems based on a programmable logic controller (PLC)

This paper introduces a Software Design Specification and Analysis Technique (SDSAT) for safety-critical systems based on a Programmable Logic Controller (PLC). During software development phases, the design phase performs an important role in connecting the requirements phase and the implementation phase, and it is a process of translating software requirements into software structures. In this work, the Nuclear FBD-style Design Specification and analysis (NuFDS) approach was proposed for nuclear Instrumentation and Control (I&C) software. The NuFDS approach is suggested in a straightforward manner for effective and formal software design specification and analysis. Accordingly, the proposed NuFDS approach is composed of a software design specification technique and a software design analysis technique. In addition, for tool support in the design phase, we developed the NuSDS tool based on the NuFDS approach; this tool is used specifically for generating software design specification and analysis for nuclear fields.     

Role of Brassica juncea (L.) Czern. (var. Vaibhav) in the phytoextraction of Ni from soil amended with fly ash: selection of extractant for metal bioavailability

A pot experiment was carried out to study the potential of the plant of Brassica juncea for the phytoextraction of metal from fly ash amended soil and to study correlation between different pool of metals (total, DTPA, CaCl(2) and NH(4)NO(3)) and metal accumulated in the plant in order to assess better extractant for plant available metals. The results of total metal analysis in the soil revealed the presence of Cr, which was found below detection limit (BDL) in the plants. The fly ash (FA) amendments and soil samples were extracted with different extractants and the level of metal vary from one extractant to another. The regression analysis between total and extractable metals showed better regression for all the tested metals except Mn (R(2)=0.001) in DTPA extraction. Correlation coefficient between metal accumulation by the plant tissues and different pool of metals showed better correlation with DTPA in case of Fe, Zn and Ni, whereas, Cu was significantly correlated with NH(4)NO(3) and other metals (Pb, Mn) with CaCl(2). The soil analysis results revealed that the mobility and plant availability of metals (Fe, Mn, Zn, Ni) within the profiles of amended soils was influenced by the change in pH, however, Pb and Cu was not affected. The metal accumulation in total plant tissues was found in the order of Fe>Ni>Zn>Mn>Cu>Pb and its translocation was found more in upper part. The plants grown on soil amended with 25%FA have shown significant increase in plant biomass, shoot and plant height, whereas, no significant effect was observed in root length. The cluster analysis showed 10%FA behave differently on the basis of physico-chemical properties and metal behavior. Thus, it may be concluded that B. juncea can be used for phytoextraction of metals, especially Ni in fly ash amendment soil.     

Analysis according to the change in structure of ultrasonic sensors and the change in pressure inside the tank for measurement of fuel amounts in compressed natural gas (CNG) tank

The present study is a fundamental research for precise measurement of fuel amounts in a compressed natural gas (CNG) tank where an analysis of receiving sensitivity was conducted as a result of changes in the contact surface shape in the number of piezoelectric element of the ultrasonic sensor as well as in the internal pressure of the tank. Experiments were conducted as a function of changes in the contact surface shape between the ultrasonic sensor and outside of the aluminum tank and in the number of piezoelectric element as well as in the internal pressure of the tank. According to the experimental results, it could be confirmed that the maximum receiving sensitivity value was increased by about 60 % when the contact surface shape of the transmission and receiving ultrasonic sensors compared with the ultrasonic sensor in the Line-Line shape selected as the reference model was changed to the surface. As a whole, the highest receiving sensitivity values were observed when the transmission sensor of surface shape produced as multiple piezoelectric elements and the receiving sensor of surface shape produced as a single piezoelectric element were used. It could be confirmed that receiving sensitivities were improved at the same voltage value as a result of changes in the contact surface shape of the ultrasonic sensor and in the number of piezoelectric elements.     

Determination of the total acid number (TAN) of used gas engine oils by IR and chemometrics applying a combined strategy for variable selection

The most common analyses carried out to assess gas engine oil quality include determination of viscosity, total base number (TBN), and total acid number (TAN). TAN has been considered to be an important indicator of oil quality, specifically in terms of defining oxidation and the extent of acidic contamination of used oils. TAN can be determined by potentiometric titration, and typical values for used oils can reach up to 4 mg KOH/g. A more convenient approach for the determination of TAN is based on infrared (IR) spectral data and multivariate regression models.We developed partial least-squares (PLS) models for the determination of TAN using IR data measured from monograde mineral gas engine oils (SAE 40, medium ash) that have been used in sewer and wood gas engines run with gaseous fuels from a sewage plant and a wood gasification plant, respectively. The final model performance was 0.07 mg KOH/g for the standard error of prediction (SEP). Essential for the development of powerful empirical models was an appropriate variable selection by combining expert knowledge, biPLS or dyn-biPLS, and a genetic algorithm. The optimum complexities of the models (the number of PLS components) and their prediction performances have been estimated by repeated double cross validation (rdCV).     

Superhard phases of simple substances and binary compounds of the B-C-N-O system: from diamond to the latest results (a Review)

The basic known and hypothetic one- and two-element phases of the B-C-N-O system (both superhard phases having diamond and boron structures and precursors to synthesize them) are described. The attention has been given to the structure, basic mechanical properties, and methods to identify and characterize the materials. For some phases that have been recently described in the literature the synthesis conditions at high pressures and temperatures are indicated.     

Sequential eluent injection technique as a new approach for the on-line enrichment and speciation of Cr(III) and Cr(VI) species on a single column with FAAS detection

This paper introduces a sequential eluent injection (SEI) technique combined with an on-line preconcentration/separation system for a fast and sensitive FAAS determination of trace amounts of Cr(III) and Cr(VI) species. The method is based on the simultaneous retention of Cr(III) and Cr(VI) on a single mini-column packed with a chloromethylated polystyrene functionalized with N,N-bis(naphthylideneimino)diethylenetriamine (PS-NAPdien) at pH 6.7. The retained chromium species was eluted by sequential injection of HCl for desorption of Cr(III), and NH₃ and NH₄NO₃ buffer solutions for desorption of Cr(VI). All the chemical and flow injection variables were optimized for the quantitative preconcentration and speciation of Cr(III) and Cr(VI). Under the optimum conditions, the calibration graph obtained is linear over the concentration range of 2.0-60.0 μg L⁻¹ for Cr(III), and 8.0-180.0 μg L⁻¹ for Cr(VI). The preconcentration factors for Cr(III) and Cr(VI) were 70 and 30, respectively. The 3σ detection limits were 0.6 μg L⁻¹ and 2.5 μg L⁻¹ for Cr(III) and Cr(VI), respectively. The relative standard deviations were 2.55% and 0.8%, respectively, for 6 replicate determinations of Cr(III) and Cr(VI) at the 40.0 μg L⁻¹ level. The proposed method was applied for determination of Cr(III) and Cr(VI) in different water samples with satisfactory results.