Plantwide operability assessment for nonlinear processes using a microscopic level network analysis

The increase of raw material and energy costs has caused a shift in process design philosophy leading to more complex chemical plants utilising heat integration and material recycles. This warrants plantwide dynamic operability analysis in the process design stage. In our previous work, a networked plantwide operability analysis approach was developed, where the plantwide process is viewed as a network of process units connected via mass and energy flow. Such an analysis is based on the dissipativity of each process unit and the topology of the process network. However, to determine the dissipativity of multivariable nonlinear process units is often extremely difficult. In this work, we take the network approach to a microscopic level and treat each nonlinear multivariable process unit as a network of individual (single state) mass and energy balances (sub-systems). The plantwide process is then viewed as a network of such sub-systems rather than physical process units. The dissipativity of these simple sub-systems can often be determined more easily in comparison to that of multivariable sub-systems. The dissipativity property (in terms of supply rate) of the entire nonlinear process can be parametrised by the dissipativity of individual sub-systems, leading to a cluster of supply rates. The operability of the plantwide nonlinear process can then be determined based on the above parametrised dissipativity which can be much less conservative than existing nonlinear analysis. The effects of interactions caused by the interconnections are considered explicitly based on the network topology. The stability and stabilisability analysis problem is then converted into a feasibility problem with linear matrix inequalities which can be solved numerically. The application of the proposed approach requires successful determination of the dissipativity of nonlinear sub-systems.     

Dynamic operability analysis of nonlinear process networks based on dissipativity

Most modern chemical plants are complex networks of multiple interconnected nonlinear process units, often with multiple recycle and by‐pass streams and energy integration. Interactions between process units often lead to plant‐wide operability problems (i.e., difficulties in process control). Plant‐wide operability analysis is often difficult due to the complexity and nonlinearity of the processes. This article provides a new framework of dynamic operability analysis for plant‐wide processes, based on the dissipativity of each process unit and the topology of the process network. Based on the concept of dissipative systems, this approach can deal with nonlinear processes directly. Developed from a network perspective, the proposed framework is also inherently scalable and thus can be applied to large process networks. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Thermohydraulic and entropy characteristics of Al2O3-water nanofluid in a ribbed interrupted microchannel heat exchanger

In this study, an interrupted microchannel heat sink with rib turbulators was studied for its thermohydraulic effectiveness and entropy generation in a compact space. The rib edges are modified to enhance the overall functioning of the system by reducing the pressure drop. The working fluid used was Al₂O₃-water nanofluid, and increasing the Reynolds number and nanoparticle concentration triggered a reduction in the heat sink's maximum temperature. These also offer a decrease in resistance to heat transfer, and there is an improvement in the evenness of the temperature of the interrupted microchannel heat sink, as regions with the likelihood of hot spot reduced drastically. At Re = 100, increasing the nanoparticle concentration from 0% to 4% enhanced the heat transfer coefficient by 38.41% for the interrupted microchannel heat sink-base (IMCH-B) configuration. Under similar conditions, the convective heat transfer coefficient for the interrupted microchannel heat sink-fillet (IMCH-F) increased by 43.69%. Furthermore, at 0.5% concentration, changing the Reynolds number from 100 to 700 augmented the heat transfer coefficient by 70.65%. Thus, the maximum temperature of the substrate's bottom surface was reduced by 53.83°C when the system was operated at Re = 700 and nanoparticle concentration of 4%. The IMCH-C also showed relatively close results at all observed volume fractions. For the IMCH-C, the maximum temperature of the bottom surface was reduced by 41.98°C at Re = 700 when compared with Re = 100% and 4% concentration. Although at high Reynolds numbers and concentrations, the pressure drops are higher, the performance enhancement criteria prove that the nanofluid is superior to water and the edge modifications show significant performance improvement. More importantly, the IMCH-F heat sink showed the optimum performance based on the performance evaluation criteria at Re = 300 and $\phi =2\%$ (ie, at this point, the heat transfer coefficient is maximum and the pressure drop is minimum). On the other hand, the optimal thermodynamic performance was observed at Re = 700 and $\phi =4\%$. The numerical results demonstrated a potential way to exploit nano-suspensions for thermal applications, especially for high-energy flux systems with compact space constraints.     

Oxidation of ZrB2–SiC ultra-high temperature composites over a wide range of SiC content

The oxidation performance of ZrB₂–SiC ultra-high temperature ceramics with SiC content ranging from 20 to 80 vol% has been evaluated at 1773 K for 50 h and at 2073 K for 20 min. Oxidation reaction pathways were interpreted using volatility diagrams of the ZrB₂–SiC system. At 1773 K for 50 h, all ZrB₂–SiC composites from 20 to 80 vol% SiC formed a protective SiO₂ surface coating. Samples with ≤50 vol% SiC developed a distinguishable SiC-depleted layer at 1773 K and 2073 K. High temperature torch testing for 20 min at approximately 2073 K revealed that samples with ≥65 vol% SiC exhibit a depression under the torch flame. Samples rich in ZrB₂ were dominated by a ZrO₂ layer after a similar exposure. The overall weight density of ultra-high temperature ceramics can be reduced with improved oxidation performance at 1773 K by adding at least 65 vol% SiC.     

A combination of max‐type and distance based schemes for simultaneous monitoring of time between events and event magnitudes

Traditionally, two isolated sequential stopping rules are employed for monitoring the time of occurrence of an event (T) and the magnitude of an event (X) . Recently, several researchers recommend monitoring T and X together using some unified approach. A unified approach based on combinations of two statistics, one for monitoring T and the other for X , is often more efficient. Likewise, a new approach of simultaneous monitoring of location and scale parameters of a process, combining a max and a distance based statistics, is recently introduced in literature. Motivated by such emerging concepts, we design a new scheme combining a Max‐type and a Distance‐type schemes, referred to as the MT scheme, to monitor T  and X simultaneously and efficiently. It retains the advantages of both the Max‐type and the Distance‐type schemes for joint inference. The proposed scheme is very competent in detecting a shift in the process distribution of T  or X or both. Moreover, it is computationally simpler. It has nice exact expressions for design parameters. Therefore, it is easier to implement. It has a distinct advantage over its traditional counterparts in detecting moderate to large shifts. Finally, we illustrate the implementation of the proposed scheme with a real dataset of damage caused by outbreak of fire disaster.     

Advanced CNC system with in-process feed-rate optimisation

Tight quality requirements and stringent customer demands are the main thrust behind the development of new generation machine tool controllers that are more universal, adaptable and interoperable. The development of some international standards such as STEP and STEP-NC presents a vision for intelligent CNC machining. Implementation of STEP-NC enabled Machine Condition Monitoring (MCM) is presented in this paper. The system allows optimisation during machining in order to shorten machining time and increase product quality. In the system, an optiSTEP-NC, an AECopt controller and a Knowledge-Based Evaluation (KBE) module have been developed. The aim of the optiSTEP-NC system is to perform initial feed-rate optimisation based on STEP-NC data to assist process planners in assigning appropriate machining parameters. AECopt acts as a connector between the process planner and machining environment with the intention to provide adaptive and automatic in-process machining optimisation. KBE based-MTConnect is responsible for obtaining machining know-how. Optimisation is performed before, during or after machining operations, based on the data collected and monitored such as machining vibration, acceleration and jerk, cutting power and feed-rate.     

Mechanical,thermal, tribological,and flammability properties of polybutylene terephthalate composites: Comparing the effects of synthetic wollastonite nanofibers,natural wollastonite,and graphene oxide

Polybutylene terephthalate (PBT) composites were prepared with 1.0 phr synthetic wollastonite nanofibers (SWN), natural wollastonite (NW) and graphene oxide (GO) to study the effect of different fillers on mechanical, thermal, tribological, and flammability properties. The properties of PBT composites are related to the size, structure, and interfacial adhesion of the fillers in PBT matrix. PBT/SWN demonstrated the highest tensile strength and Young's modulus (6% and 9% increment), followed by PBT/NW (1.3% and 7% increment) and PBT/GO (2% decrement and 4% increment). PBT/SWN gave the highest degradation temperature (409°C), followed by PBT/GO (404.7°C). The maximum enhancement in wear resistance (73%) by PBT/SWN and anti-friction performance (26%) by PBT/GO evinced the excellent load-bearing ability of SWN and the great lubricating effect of GO. PBT/NW had the lowest peak heat release rate, smoke, and carbon dioxide production rate. This study shows that PBT composites have great potential in different automotive applications.     

Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

In this study, a new hybrid model integrated adaptive neuro fuzzy inference system with Firefly Optimization algorithm (ANFIS-FFA), is proposed for forecasting monthly rainfall with one-month lead time. The proposed ANFIS-FFA model is compared with standard ANFIS model, achieved using predictor-predictand data from the Pahang river catchment located in the Malaysian Peninsular. To develop the predictive models, a total of fifteen years of data were selected, split into nine years for training and six years for testing the accuracy of the proposed ANFIS-FFA model. To attain optimal models, several input combinations of antecedents’ rainfall data were used as predictor variables with sixteen different model combination considered for rainfall prediction. The performances of ANFIS-FFA models were evaluated using five statistical indices: the coefficient of determination (R ² ), Nash-Sutcliffe efficiency (NSE), Willmott’s Index (WI), root mean square error (RMSE) and mean absolute error (MAE). The results attained show that, the ANFIS-FFA model performed better than the standard ANFIS model, with high values of R ² , NSE and WI and low values of RMSE and MAE. In test phase, the monthly rainfall predictions using ANFIS-FFA yielded R ² , NSE and WI of about 0.999, 0.998 and 0.999, respectively, while the RMSE and MAE values were found to be about 0.272 mm and 0.133 mm, respectively. It was also evident that the performances of the ANFIS-FFA and ANFIS models were very much governed by the input data size where the ANFIS-FFA model resulted in an increase in the value of R ² , NSE and WI from 0.463, 0.207 and 0.548, using only one antecedent month of data as an input (t-1), to almost 0.999, 0.998 and 0.999, respectively, using five antecedent months of predictor data (t-1, t-2, t-3, t-6, t-12, t-24). We ascertain that the ANFIS-FFA is a prudent modelling approach that could be adopted for the simulation of monthly rainfall in the present study region.