Performance Of Batch Processes Research Articles

Conversion of Syngas from Entrained Flow Gasification of Biogenic Residues with Clostridium carboxidivorans and Clostridium autoethanogenum

Synthesis gas fermentation is a microbial process, which uses anaerobic bacteria to convert CO-rich gases to organic acids and alcohols and thus presents a promising technology for the sustainable production of fuels and platform chemicals from renewable sources. Clostridium carboxidivorans and Clostridium autoethanogenum are two acetogenic bacteria, which have shown their high potential for these processes by their high tolerance toward CO and in the production of industrially relevant products such as ethanol, 1-butanol, 1-hexanol, and 2,3-butanediol. A promising approach is the coupling of gasification of biogenic residues with a syngas fermentation process. This study investigated batch processes with C. carboxidivorans and C. autoethanogenum in fully controlled stirred-tank bioreactors and continuous gassing with biogenic syngas produced by an autothermal entrained flow gasifier on a pilot scale >1200 °C. They were then compared to the results of artificial gas mixtures of pure gases. Because the biogenic syngas contained 2459 ppm O2 from the bottling process after gasification of torrefied wood and subsequent syngas cleaning for reducing CH4, NH3, H2S, NOX, and HCN concentrations, the oxygen in the syngas was reduced to 259 ppm O2 with a Pd catalyst before entering the bioreactor. The batch process performance of C. carboxidivorans in a stirred-tank bioreactor with continuous gassing of purified biogenic syngas was identical to an artificial syngas mixture of the pure gases CO, CO2, H2, and N2 within the estimation error. The alcohol production by C. autoethanogenum was even improved with the purified biogenic syngas compared to reference batch processes with the corresponding artificial syngas mixture. Both acetogens have proven their potential for successful fermentation processes with biogenic syngas, but full carbon conversion to ethanol is challenging with the investigated biogenic syngas.

Continuous sulfide supply enhanced autotrophic production of alcohols with Clostridium ragsdalei

Autotrophic syngas fermentation with clostridia enables the conversion of CO, CO2, and H2 into organic acids and alcohols. The batch process performance of Clostridium ragsdalei was studied in fully controlled and continuously gassed (600 mbar CO, 200 mbar H2, 200 mbar CO2) stirred-tank bioreactors. The final ethanol concentration varied as function of the reaction conditions. Decreasing the pH from pH 6.0–5.5 at a temperature of 37 °C increased the ethanol concentration from 2.33 g L−1 to 3.95 g L−1, whereas lowering the temperature from 37 to 32 °C at constant pH 6.0 resulted in a final ethanol concentration of 5.34 g L−1 after 5 days of batch operation. The sulphur availability was monitored by measuring the cysteine concentration in the medium and the H2S fraction in the exhaust gas. It was found that most of the initially added sulphur was stripped out within the first day of the batch process (first half of the exponential growth phase). A continuous sodium sulfide feed allowed ethanol concentrations to increase more than threefold to 7.67 g L−1 and the alcohol-to-acetate ratio to increase 43-fold to 17.71 g g−1.Graphical

D-Galacturonic acid reduction by S. cerevisiae for L-galactonate production from extracted sugar beet press pulp hydrolysate

Pectin-rich residues are considered as promising feedstocks for sustainable production of platform chemicals. Enzymatic hydrolysis of extracted sugar beet press pulp (SBPP) releases the main constituent of pectin, d-galacturonic acid (d-GalA). Using engineered Saccharomyces cerevisiae, d-GalA is then reduced to l-galactonate (l-GalOA) with sorbitol as co-substrate. The current work addresses the combination of enzymatic hydrolysis of pectin in SBPP with a consecutive optimized biotransformation of the released d-GalA to l-GalOA in simple batch processes in stirred-tank bioreactors. Process conditions were first identified with synthetic media, where a product concentration of 9.9 g L-1 L-GalOA was obtained with a product selectivity of 99% (L-GalOA D-GalA-1) at pH 5 with 4% (w/v) sorbitol within 48 h. A very similar batch process performance with a product selectivity of 97% was achieved with potassium citrate buffered SBPP hydrolysate, demonstrating for the first time direct production of L-GalOA from hydrolyzed biomass using engineered S. cerevisiae. Combining the hydrolysis process of extracted SBPP and the biotransformation process with engineered S. cerevisiae paves the way towards repurposing pectin-rich residues as substrates for value-added chemicals.Key points• Efficient bioreduction of D-GalA with S. cerevisiae in stirred-tank reactors• Batch production of L-GalOA by engineered S. cerevisiae with high selectivity• Direct L-GalOA production from hydrolyzed sugar beet press pulpGraphical abstractBioreduction of D-galacturonic acid to L-galactonate with recombinant Saccharomyces cerevisiae enables for the first time the valorization of hydrolysates from extracted sugar beet press pulp for the sustainable production of value-added chemicals.

Two-Dimensional Iterative Learning Model Predictive Control for Batch Processes: A New State Space Model Compensation Approach

To achieve improved control performance of batch processes under uncertainty, a novel two-dimensional model predictive iterative learning control (2D-MPILC) scheme is proposed. First, a new two-dimensional (2-D) extended nonminimal state space model is formulated where more degrees of freedom are offered for further controller design; second, a new error compensation strategy is introduced in the controller design to improve the ensemble control performance. The two merits are combined together to form a new control strategy where the model predictive control and iterative learning control are united based on the 2-D framework. By employing the novel model formulation and the system error compensation approaches, the effects caused by uncertainty in the batch processes can be eliminated gradually from cycle-to-cycle such that the desired control performance will be obtained finally. The effectiveness of the proposed 2D-MPILC is tested on the packing pressure in the injection molding batch process.

Reaction engineering analysis of Scenedesmus ovalternus in a flat-plate gas-lift photobioreactor

Microalgal strains of the genus Scenedesmus are a promising resource for commercial biotechnological applications. The temperature-, pH- and light-dependent growth of Scenedesmus ovalternus has been investigated on a laboratory scale. Best batch process performance was obtained at 30°C, pH 8.0 and an incident photon flux density of 1300μmolphotonsm−2s−1 using a flat-plate gas-lift photobioreactor. Highest growth rate (0.11h−1) and space-time yield (1.7±0.1gCDWL−1d−1) were observed when applying these reaction conditions. Biomass concentrations of up to 7.5±0.1gCDWL−1 were achieved within six days (25.0±0.5gCDWm−2d−1). The light-dependent growth kinetics of S. ovalternus was identified using Schuster’s light transfer model and Andrews’ light inhibition model (KS=545μmolphotonsm−2s−1; KI=2744μmolphotonsm−2s−1; μmax=0.21h−1). The optimal mean integral photon flux density for growth of S. ovalternus was estimated to be 1223μmolphotonsm−2s−1.

On the Use of Nonlinear Model Predictive Control without Parameter Adaptation for Batch Processes

Optimization techniques are typically used to improve economic performance of batch processes, while meeting product and environmental specifications and safety constraints. Offline methods suffer from the parameters of the model being inaccurate, while re-identification of the parameters may not be possible due to the absence of persistency of excitation. Thus, a practical solution is the Nonlinear Model Predictive Control (NMPC) without parameter adaptation, where the measured states serve as new initial conditions for the re-optimization problem with a diminishing horizon. In such schemes, it is clear that the optimum cannot be reached due to plant-model mismatch. However, this paper goes one step further in showing that such re-optimization could in certain cases, especially with an economic cost, lead to results worse than the offline optimal input. On the other hand, in absence of process noise, for small parametric variations, if the cost function corresponds to tracking a feasible trajectory, re-optimization always improves performance. This shows inherent robustness associated with the tracking cost. A batch reactor example presents and analyzes the different cases. Re-optimizing led to worse results in some cases with an economical cost function, while no such problem occurred while working with a tracking cost.

Non-water miscible ionic liquid improves biocatalytic production of geranyl glucoside with Escherichia coli overexpressing a glucosyltransferase

Whole cells of Escherichia coli overexpressing a glucosyltransferase from Vitis vinifera were used for the glucosylation of geraniol to geranyl glucoside. A high cell density cultivation process for the production of whole-cell biocatalysts was developed, gaining a dry cell mass concentration of up to 67.6±1.2gL(-1) and a glucosyltransferase concentration of up to 2.7±0.1g protein L(-1) within a process time of 48h. Whole-cell batch biotransformations in milliliter-scale stirred-tank bioreactors showed highest conversion of geraniol at pH 7.0 although the pH optimum of the purified glucosyltransferase was at pH 8.5. The biocatalytic batch process performance was improved significantly by the addition of a water-immiscible ionic liquid (N-hexylpyridinium bis(trifluoromethylsulfonyl)imid) for in situ substrate supply. The so far highest final geranyl glucoside concentration (291±9mgL(-1)) and conversion (71±2%) reported for whole-cell biotransformations of geraniol were achieved with 5% (v/v) of the ionic liquid.

Robustness of reinforced gradient-type iterative learning control for batch processes with Gaussian noise

In this paper, a reinforced gradient-type iterative learning control profile is proposed by making use of system matrices and a proper learning step to improve the tracking performance of batch processes disturbed by external Gaussian white noise. The robustness is analyzed and the range of the step is specified by means of statistical technique and matrix theory. Compared with the conventional one, the proposed algorithm is more efficient to resist external noise. Numerical simulations of an injection molding process illustrate that the proposed scheme is feasible and effective.

A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation

Nonlinear model predictive control (NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies: a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of simplified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The method is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.

Quality relevant nonlinear batch process performance monitoring using a kernel based multiway non-Gaussian latent subspace projection approach

Multiway kernel partial least squares method (MKPLS) has recently been developed for monitoring the operational performance of nonlinear batch or semi-batch processes. It has strong capability to handle batch trajectories and nonlinear process dynamics, which cannot be effectively dealt with by traditional multiway partial least squares (MPLS) technique. However, MKPLS method may not be effective in capturing significant non-Gaussian features of batch processes because only the second-order statistics instead of higher-order statistics are taken into account in the underlying model. On the other hand, multiway kernel independent component analysis (MKICA) has been proposed for nonlinear batch process monitoring and fault detection. Different from MKPLS, MKICA can extract not only nonlinear but also non-Gaussian features through maximizing the higher-order statistic of negentropy instead of second-order statistic of covariance within the high-dimensional kernel space. Nevertheless, MKICA based process monitoring approaches may not be well suited in many batch processes because only process measurement variables are utilized while quality variables are not considered in the multivariate models. In this paper, a novel multiway kernel based quality relevant non-Gaussian latent subspace projection (MKQNGLSP) approach is proposed in order to monitor the operational performance of batch processes with nonlinear and non-Gaussian dynamics by combining measurement and quality variables. First, both process measurement and quality variables are projected onto high-dimensional nonlinear kernel feature spaces, respectively. Then, the multidimensional latent directions within kernel feature subspaces corresponding to measurement and quality variables are concurrently searched for so that the maximized mutual information between the measurement and quality spaces is obtained. The I2 and SPE monitoring indices within the extracted latent subspaces are further defined to capture batch process faults resulting in abnormal product quality. The proposed MKQNGLSP method is applied to a fed-batch penicillin fermentation process and the operational performance monitoring results demonstrate the superiority of the developed method as apposed to the MKPLS based process monitoring approach.

Enhanced batch process monitoring using just-in-time-learning based kernel partial least squares

Batch process has the characteristics of dynamic behavior, strong nonlinearity and multiplicity of operation phases. In this paper, an integrated framework consisting of just-in-time-learning (JITL) method and kernel partial least squares (KPLS) is described for the monitoring of the performance of batch processes. The training data set for modeling is defined by using JITL, and KPLS is employed to build the model for process monitoring purpose. The JITL model based on local neighborhoods of similar samples is very accurate and sensitive because it can well track the change of the process. Therefore, the proposed method is suited for multiphase and time-varying batch processes. Meanwhile, KPLS is a very efficient technique for tacking complex nonlinear data sets. Furthermore, the proposed monitoring strategy does not require the estimation of future values of the process variables during online application. The effectiveness of the proposed monitoring approach is demonstrated through two sets of benchmark data. The comparison of monitoring results shows that the proposed JITL–KPLS approach is superior to multiway KPLS and hierarchical KPLS, and it can achieve accurate detection of various types of process faults occurring in the batch operation.

Deterministic vs. stochastic performance assessment of iterative learning control for batch processes

The performance assessment of linear time‐invariant batch processes when iterative learning control (ILC) is implemented has been discussed. Previous literatures show that conventional performance assessment cannot be directly applied to batch processes due to the nature of batch operations. Chen and Kong have suggested a new method to assess the control performance of batch processes using optimal ILC as the benchmark. In their work, ILC controllers are assumed to affect either stochastic or deterministic performance but without considering their interaction. This work elaborates the controllers effects on both stochastic and deterministic control performance of batch processes. It is shown that the optimal solution based on the minimum variance control law has a trade‐off between deterministic and stochastic performance, which can be shown by a trade‐off curve. Furthermore, a method is proposed to estimate this curve from routine operating data, against which the performance of ILC controllers can be assessed. Simulation studies are conducted to verify the proposed method. © 2012 American Institute of Chemical Engineers AIChE J, 59: 457–464, 2013

A PAT Approach to Enhance Process Understanding of Fluid Bed Granulation Using In-line Particle Size Characterization and Multivariate Analysis

The purpose of this study was to achieve improved process understanding of fluid bed granulation using in-line particle size analyzer in conjunction with multivariate methods. The combined use of process analyzers and multivariate tools provides a useful means to drug development within the framework of quality by design utilizing process analytical technology. The evaluation of in-line monitoring manufacturability quality attributes, particle size, and particle size distribution, was conducted using the Parsum probe which is based on spatial filtering technique. Several granulation batches were manufactured and monitored using a commercial-scale fluid bed granulator. Reference measurements by offline Malvern MasterSizer showed good agreement with those by Parsum at end-of-spray phase. Multivariate/batch statistical process control methods were used to evaluate batch process performance, batch-to-batch variation and develop potential control strategy. The results indicated that the Parsum analyzer is a viable tool for in-line particle size characterization and improved process understanding in combination with multivariate tools.

Local dynamic partial least squares approaches for the modelling of batch processes

AbstractThe application of multivariate statistical projection based techniques has been recognized as one approach to contributing to an increased understanding of process behaviour. The key methodologies have included multi‐way principal component analysis (PCA), multi‐way partial least squares (PLS) and batch observation level analysis. Batch processes typically exhibit nonlinear, time variant behaviour and these characteristics challenge the aforementioned techniques. To address these challenges, dynamic PLS has been proposed to capture the process dynamics. Likewise approaches to removing the process nonlinearities have included the removal of the mean trajectory and the application of nonlinear PLS. An alternative approach is described whereby the batch trajectories are sub‐divided into operating regions with a linear/linear dynamic model being fitted to each region. These individual models are spliced together to provide an overall nonlinear global model. Such a structure provides the potential for an alternative approach to batch process performance monitoring. In the paper a number of techniques are considered for developing the local model, including multi‐way PLS and dynamic multi‐way PLS. Utilising the most promising set of results from a simulation study of a batch process, the local model comprising individual linear dynamic PLS models was benchmarked against global nonlinear dynamic PLS using data from an industrial batch fermentation process. In conclusion the results for the local operating region techniques were comparable to the global model in terms of the residual sum of squares but for the global model structure was evident in the residuals. Consequently, the local modelling approach is statistically more robust.

Tri-linear on-line monitoring of a batch process

For assured through batch process performance monitoring, a number of bilinear and tri-linear modelling techniques require information on the entire batch duration, to enable the on-line realisation of the nominal model. Various strategies have been proposed for the in-filling of these yet unknown values. A methodology is proposed where the unknown observations are calculated from a weighted combination of the scores from a new batch and those from a reference data-set. A modification to the existing confidence limits is then described for the on-line implementation of a tri-linear model. The methodology is demonstrated on a benchmark simulation of a semi-batch emulsion polymerisation.

Evaluation of parallel operated small-scale bubble columns for microbial process development using Staphylococcus carnosus

Shake flasks and pH-controlled small-scale bubble columns were compared with respect to their usefulness as a basic tool for process development for human calcitonin precursor fusion-protein production with Staphylococcus carnosus. Parallel control of the pH (and making use of the base addition data) is necessary to study the effects of medium composition, to identify pH-optima and to develop a medium, which minimizes the acid excretion of S. carnosus. This medium with glycerol as energy source and yeast extract as carbon and nitrogen source resulted in cell dry weight concentration in shake flasks of 5 g l −1, which were thus improved by a factor of 10. Cell dry weight concentrations of up to 12.5 g l −1 were measured in the batch process with pH-controlled small-scale bubble columns due to their higher oxygen transfer capability. In contrast to shake flasks it was demonstrated, that the batch process performance of recombinant S. carnosus secreting the human calcitonin precursor fusion-protein was identical within the estimation error in pH-controlled small-scale bubble columns compared to the stirred-tank reactor.

Fermentation process kinetics

Information on fermentation process kinetics is potentially valuable for the improvement of batch process performance; it is essential for continuous process design. An empirical examination of rate patterns in various fermentations discloses three basic types: (1) ‘growth associated’ products arising directly from the energy metabolism of carbohydrates supplied, (2) indirect products of carbohydrate metabolism and (3) products apparently unrelated to carbohydrate oxidation. Effects of operating variables on the primary kinetic processes, growth, sugar utilization and antibiotic formation, in the penicillin process, illustrate the special nature of this type.

Experimental testing of flexible recipe control based on a hybrid model

Flexible recipe control is aimed at improving the performance of batch processes by changing the recipe parameters. Compensation is effected with the aid of the process model and optimisation. This strategy was used for the quality control of an industrial process in TiO 2 pigment production. The control algorithm was based on a hybrid model in order to use all the information about the process (knowledge, data, experience) and to design the model from a small number of real-plant experiments. When testing on the real process the algorithm was able to move the process output closer to the desired value.

Learning to Control the Performance of Batch Processes

The current shift in chemical industry interest away from high throughput production towards small amounts of high value added products has increased the industry's awareness of the issues associated with product and process performance. This fact poses a unique set of challenging control problems characterized by their ‘ill-definedness’, high process nonlinearities and imperfect modelling. In this work, a novel methodology is proposed for incremental learning of a control policy that can continuously improve product quality and operating performance.The new concept introduced here is the notion of a performance function that implicitly includes end-product quality constraints as a process goal and operational preferences which describe different modes of operation. Since plant information is often scarce and expensive to obtain, it is proposed that the performance function be learned by integrating together batch-to-batch data, intra-run measurements and a predictive process model. A new framework for this integration, called coarse code generalization, is proposed which revolves around the generation of an artificial set of batch runs using a hybrid process model. In this model, learning biases are incorporated through background knowledge that expresses run outcome sensitivities with respect to states and actions. Artificial batch runs provide an augmented data set which is used for inductive learning of the performance function. With a minimum amount of information on process performance available, a first approximation to the performance function is constructed and an optimization program is used to define a near-optimal control policy. As more plant data become available, the performance function refinement procedure permits also an increasing refinement of the learned control policy. Recipe changes that increase process performance can then be implemented on-line. A semi-batch reactor where an autocatalytic reaction takes place is used to present and test the methodology. Results obtained demonstrate that the methodology can cope successfully with the problems of both imperfect modelling and scarce information which are typical of the industrial environment. Also, coarse code generalization for performance control proves to be an ideal means of integrating inductive learning with first-principles models and other types of domain knowledge.

Improved batch process performance by evolutionary modelling

A key issue for improving the industrial efficiency in energy and material resources usage is to integrate the dynamics of the process and its scenario in the actual plant operation decision making. In this work, a combination of statistical techniques has been used to build an automatic modelling tool based on Neural Networks, that overruns the limitations of modelling techniques based on the theoretical knowledge of the process principles. As the overall system can be run simultaneously with the process, the tool can be used to continuously readjust its parameters and to follow evolutionary processes. The resulting model can be applied for process forecasting and control, resulting in improvements in the process performance. In the Neural Network field, a new method is introduced to test the results and a heuristic is proposed to stop the learning process when the best model has been found.