M. Nazmul Karim

Preparation of PDMS membrane using water as solvent for pervaporation separation of butanol–water mixture

Polydimethylsiloxane (PDMS) membrane has attracted increasing attention due to its potential application in separating organic–organic liquid mixtures and removing volatile organic compounds from water and soil. However, solvents like n-hexane, n-heptane and others are generally used in large amounts during its traditional preparation process. This study aimed to provide a low-pollution and high-efficiency preparation method using water as a solvent in the presence of surfactant (dodecylbenzene sulfonic acid, DBSA). Comparisons between the membranes prepared separately with the traditional method and the green method were conducted by scanning electron microscopy (SEM), atomic force microscopy (AFM), attenuated total reflection Fourier transform infrared (FTIR-ATR) spectroscopy and pervaporation (PV) experiments. The results showed that they performed basically the same in the first three aspects but displayed markedly different characteristics in the PV experiments. The separation factors of the PDMS membranes prepared using the green method for separating 1.5 wt% n-butanol aqueous solution at 55 °C increased by 30–53% relative to those of membranes prepared using the traditional method, while the total flux only decreased by 7–10%. These performance improvements resulted from the shortening of evaporation time induced by the decrease of n-hexane content. Further, this hypothesis was confirmed by the performance of membranes prepared using the green method, from angles of crosslinking density, water contact angle and swelling degree (SD). Comparison with previous reports on PV performance of PDMS membranes implied that the green method was not only environment-friendly and economically competitive but also led to enhanced PV performance.

A PDMS membrane with high pervaporation performance for the separation of furfural and its potential in industrial application

Producing furfural based on the hydrolysis of biomass rich in hemicellulose is a sustainable technique; however, separating furfural from the hydrolysate by the conventional methods like distillation is energy-intensive and environmentally unfriendly. Pervaporation, an energy-efficient ‘clean technology’, is thus suggested for the separation of furfural. A polydimethylsiloxane (PDMS) membrane was prepared using water as a solvent in the presence of a surfactant (sodium dodecyl sulfate, SDS). Effects of feed concentration and temperature, thickness of the PDMS layer and operation time on pervaporation performance were investigated, and the apparent activation energies of furfural and water permeating through the membrane were calculated according to the Arrhenius-type equation. Permeate concentration and furfural flux reached 62.4 wt% and 3222.6 g m−2 h−1 when separating 6.5 wt% furfural aqueous solution at 95 °C. Compared to distillation, pervaporation by the PDMS membrane provided higher selectivity while consuming 70% less evaporation energy. Additionally, the PDMS membrane displayed more promising potential in industrial application than those reported in the literature due to its higher furfural flux.

A novel method for furfural recovery via gas stripping assisted vapor permeation by a polydimethylsiloxane membrane

Furfural is an important platform chemical with a wide range of applications. However, due to the low concentration of furfural in the hydrolysate, the conventional methods for furfural recovery are energy-intensive and environmentally unfriendly. Considering the disadvantages of pervaporation (PV) and distillation in furfural separation, a novel energy-efficient ‘green technique’, gas stripping assisted vapor permeation (GSVP), was introduced in this work. In this process, the polydimethylsiloxane (PDMS) membrane was prepared by employing water as solvent. Coking in pipe and membrane fouling was virtually non-existent in this new process. In addition, GSVP was found to achieve the highest pervaporation separation index of 216200 (permeate concentration of 71.1u2005wt% and furfural flux of 4.09u2005kgm−2h−1) so far, which was approximately 2.5 times higher than that found in pervaporation at 95°C for recovering 6.0u2005wt% furfural from water. Moreover, the evaporation energy required for GSVP decreased by 35% to 44% relative to that of PV process. Finally, GSVP also displayed more promising potential in industrial application than PV, especially when coupled with the hydrolysis process or fermentation in biorefinery industry.

Separating isopropanol from its diluted solutions via a process of integrating gas stripping and vapor permeation

Considering environmental pollution, disposal costs, the high-value of isopropanol (IPA) and other factors, recovering isopropanol from industrial effluent is considered to be attractive, practical and cost-effective. However, the separation techniques including gas stripping, distillation, and pervaporation often yield low selectivity and high energy consumption. In this paper, a process of integrating gas stripping and vapor permeation was conducted for separating isopropanol from dilute solutions. A PDMS (polydimethylsiloxane) membrane was prepared by using a green method. The effects of gas flow rate, membrane model temperature, feed solution temperature, and feed solution concentration on the performance of the separation system were investigated. The results in this study showed that the optimized separation performance (isopropanol flux 437.8 g m−2 h−1, separation factor 125.8) was obtained for separating 3 wt% isopropanol solution at 75 °C, which were 1.48 times and 7.4 times of those obtained in the PV process. The energy consumption of evaporation was only 1.28 MJ kg−1; this was 26% and 30% of the evaporation energy needed for the PV process and the distillation process at the same conditions. Additionally, a comparison of separation performance with other separation techniques was also conducted in the study.

Process monitoring using PCA-based GLR methods: A comparative study

Abstract Statistical process monitoring is a key requirement for many industrial processes. Many of these processes utilize Principal Component Analysis (PCA) in order to carry out statistical process monitoring due to its computational simplicity. Two fault detection charts that are commonly used with the PCA method are the Hotelling T2 and Q statistics. Although these charts are reasonably able to detect most shifts in the process mean, they may be unable to accurately detect other process faults, such as shifts in the process variance. Hypothesis testing methods such as the Generalized Likelihood Ratio (GLR) chart have been developed in order to detect different types of deviations from normal operating conditions, i.e., process faults. Although, GLR charts have shown superior performance in terms of fault detection compared to other existing techniques, literature has only examined the performance of a PCA-based GLR chart designed to detect shifts in the mean. Through a simulated synthetic example, this work evaluates the performance of PCA-based GLR charts designed to independently detect a shift in the mean, independently detect a shift in the variance, and simultaneously detect shifts in the mean and/or variance. The results show that in order to detect a shift in the mean or a shift in the variance, the GLR charts designed to independently detect either type of fault need to be implemented in parallel as they provide significantly lower missed detection rates, than a GLR chart designed to detect shifts in both the process mean and variance simultaneously. The GLR chart designed to simultaneously detect both a shift in the mean and variance does not perform as well as the other GLR charts, since two parameters (both the mean and variance) need to be estimated for this method while maximizing the GLR statistic, as opposed to just a single parameter for the other GLR charts. The practical applicability of the PCA-based GLR charts is demonstrated through the well-known benchmark Tennessee Eastman process, and through a case where autocorrelation is present in the data. Therefore, in order to detect shifts in the mean and/or variance, we recommend parallel implementation of the GLR charts designed to independently detect shifts in the mean, and independently detect shifts in the variance Parallel implementation of these two GLR charts aids with fault classification as well. This work also discusses the importance of selecting an appropriate window length of previous data to be used when computing the maximum likelihood estimates (MLEs) and maximizing the GLR statistic, keeping all fault detection criteria in mind in order to ensure that the desired fault detection results are obtained.

Monitoring of chemical processes using improved multiscale KPCA

Statistical process monitoring charts are critical in ensuring safety for many chemical processes. Principal Component Analysis (PCA) is often used, due to its computational simplicity. However, many chemical processes may be inherently nonlinear, and this degrades the performance of the linear PCA method. Kernel Principal Component Analysis (KPCA) is an extension of the conventional PCA chart, which can help deal with nonlinearity in a given process. Additionally, PCA assumes that process data are Gaussian and uncorrelated, and only contain a moderate level of noise. These assumptions do not usually hold in practice. Multiscale wavelet-based data representation produces wavelet coefficients that possess characteristics that are able to handle violations in these assumptions. A multiscale kernel principal component analysis (MSKPCA) method has already been developed to tackle all of these issues, but it usually provides a high false alarm rate. In this paper, an improved MKSPCA chart is developed in order to deal with the false alarm rate issue, by smoothening the detection statistic using a mean filter. The advantages brought forward by the improved method are demonstrated through a simulated example in which the developed fault detection method is used to monitor a continuous stirred tank reactor (CSTR). The results clearly show that the improved MSKPCA method provides lower missed detection and false alarm rates as well as ARL1 values compared to those provided by the conventional methods.

Economic improvement of continuous pharmaceutical production via the optimal control of a multifeed bioreactor

Projections on the profitability of the pharmaceutical industry predict a large amount of growth in the coming years. Stagnation over the last 20 years in product development has led to the search for new processing methods to improve profitability by reducing operating costs or improving process productivity. This work proposes a novel multifeed bioreactor system composed of independently controlled feeds for substrate(s) and media used that allows for the free manipulation of the bioreactor supply rate and substrate concentrations to maximize bioreactor productivity and substrate utilization while reducing operating costs. The optimal operation of the multiple feeds is determined a priori as the solution of a dynamic optimization problem using the kinetic models describing the time‐variant bioreactor concentrations as constraints. This new bioreactor paradigm is exemplified through the intracellular production of beta‐carotene using a three feed bioreactor consisting of separate glucose, ethanol and media feeds. The performance of a traditional bioreator with a single substrate feed is compared to that of a bioreactor with multiple feeds using glucose and/or ethanol as substrate options. Results show up to a 30% reduction in the productivity with the addition of multiple feeds, though all three systems show an improvement in productivity when compared to batch production. Additionally, the breakeven selling price of beta‐carotene is shown to decrease by at least 30% for the multifeed bioreactor when compared to the single feed counterpart, demonstrating the ability of the multifeed reactor to reduce operating costs in bioreactor systems.

Fault detection of nonlinear systems using an improved KPCA method

Statistical control charts are essential to ensure both safety and efficient operation of many industrial processes. Many dimensionality reduction techniques such as principal component analysis (PCA) and Partial Least Squares (PLS) regression exist, and are often employed for modeling purposes as they are relatively easy to compute. However, these techniques are only effective for modeling and monitoring linear processes. The Kernel Principal Component Analysis (KPCA) method is an extension of PCA that helps deal with any nonlinearities in the process data. However, KPCA-based fault detection methods may result in a higher false alarm rate than the conventional method. In this paper, an improved KPCA method is developed in order to tackle the issue of high false alarm rates, by utilizing a mean filter to smoothen the detection statistics that are obtained from the KPCA method. The advantages presented by the developed method are illustrated using a simulated nonlinear model. The results clearly show that the improved KPCA method provides improved fault detection results with low missed detection and false alarm rates, and smaller ARL1 values compared to the conventional methods.

Separation and recovery of intracellular beta-carotene using a process synthesis framework

Abstract In this work, the process synthesis problem for the bio-manufacturing of high-value intracellular compounds is addressed using a systematic framework that allows for the user to input key process parameters from literature or experiments. The framework is based on a superstructure optimization approach and integrates various methods and tools, including a generic model and a database for data management (Bertran et al., 2017). We propose the following five steps: (1) problem formulation, (2) data collection and superstructure generation, (3) solution of the optimization problem, and (4) sensitivity analysis and (5) experimentation with informed design and then determination of the optimal process design. The framework is implemented in Super-O, software which guides the user through the formulation and solution of synthesis problems. This paper demonstrates the proposed framework though an illustrative case study on the production of beta-carotene from recombinant Saccharomyces cerevisiae (SM14) via continuous cultivation.

Modelling and Monitoring of Natural Gas Pipelines: New Method for Leak Detection and Localization Estimation

Abstract Leakage of the chemicals from pipelines is the biggest safety concern about the transportation of the chemicals. Model-based fault detection method is one of the most widely used software solutions for fault identification. In this paper, a new leak detection method for a natural gas pipeline is proposed basing on the non-isothermal modelling of the process. The new software-based method is developed by designing an unknown input observer, which is able to deal with the disturbance from the temperature change and pressure drop from the pump station and estimate the boundary flow rate of the pipeline. A new adaptive model was updated towards the temperature and boundary pressure change for leak location estimation. A simulation of a natural gas pipeline with consumer connection was demonstrated.