Operation Of Batch Processes Research Articles

A capacitive deionization system with high energy recovery and effective re-use

We have developed a simple and inexpensive hydrophilic activated-charcoal based electrode for a CDI (capacitive deionizer) cell system and examined its physical and electrochemical properties. In this study, the ion adsorption/desorption mechanisms of a CDI stack equipped with the developed activated-charcoal electrodes, having either uni-polar or bi-polar circuit structures, were evaluated. The experimental results showed that the CDI stack with a bi-polar circuit consumes lesser energy than that with a uni-polar connection. A combined flow-through and batch processing operation for ion adsorption and desorption processes was also systematically evaluated. Furthermore, the energy recovery by discharging the stored energy of the ion desorbing electrode pair to another pair for ion adsorption by switching four-CDI cells for both the discharge and the charge sides was 70% at the maximum.

Dynamic optimization of fixed bed chemical-looping combustion systems integrated in thermal power plants

Optimization methods can be used to improve the design and operation of batch processes. In this work, we illustrate the application of dynamic simulation and optimization for power generation in chemical-looping combustion systems, carried out in batch reactors. We present a framework for the optimization strategy, in which the objective is to maximize a measure of the energy efficiency, bounded by inequality constraints reflecting the industry standards. Several case studies are used to illustrate the applicability of the present problem formulation to enhance the process performance.

Multiobjective optimization for efficient energy utilization in batch biodiesel production

Achieving environmental sustainability requires minimizing energy consumption and waste generation. Batch chemical industries are prompted to increase productivity and utilize energy more efficiently. However, in contrast to continuous processes, batch process operations are intrinsically time dependent and the multiscale nature of batch operation posts complications in implementing classical effective waste- and energy-minimization strategies. Given the complex nature of batch plants, a systematic way of identifying and evaluating suitable operating strategies is essential. In this work, we present a multiobjective optimization-based approach that integrates a detailed model of the batch process and techniques of experimental design and evolutionary optimization. The proposed concept is applied to the batch production of fatty acid methyl esters (biodiesel). The optimization of the temperature control of this process takes into account objective functions related to purity of the product, batch time, energy usage and profit. The Pareto-fronts generated by full factorial experiment and by a multiobjective evolutionary algorithm (NSGA-II) show how the objectives are correlating or conflicting. The visualization of these fronts and the optimal temperature trajectories supports the engineers and the operators to find the best trade-off among the non-dominated solutions.

Research on the Tool Management System Based on Web

The tool management is an important part in FMS (the flexible manufacturing system), especially for single or small batch processing operation plan. The scientific rationality of the tool management determines the manufacturing system reliability and production efficiency. To establish a set of tool management system based on Web, make the tool entity information of manufacturing execution system to be digitized, and realize the information sharing in the different processing unit or between enterprise alliances through the Internet, will greatly improve the production efficiency and reduce the production cost.

An ACO-based intercell scheduling approach for job shop cells with multiple single processing machines and one batch processing machine

Intercell transfers are inevitable for the manufacturing of complicated products, which disrupts the philosophy of cellular manufacturing and leads new challenges to the field of production scheduling. The issue of intercell scheduling is analyzed in the context of a cellular manufacturing system consisting of multiple single-processing machines and one batch-processing machine, which is derived from the actual manufacturing of complicated assemblies in the equipment manufacturing industry. Since the two types of machines are different from, even contrary to, each other in some constraint conditions, a combinational ant colony optimization (CACO) approach is developed in this paper, which designs two structures for the single-processing machines and the batch-processing machine, respectively. By updating pheromone trails integratedly and scheduling the single-processing operations and the batch-processing operations simultaneously, cooperative optimization for the two types of machines is achieved in the CACO. Minimizing the maximum completion time is taken as the scheduling objectives. Computational results show that the CACO has significant advantages comparing with other approaches and the CPLEX, and is especially suitable for the large dimension problems.

Consistent hierarchical economic NMPC for a class of hybrid systems using neighboring-extremal updates

A hierarchical two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems to support economically optimal operation of batch or continuous processes with a predefined production schedule. For this class of hybrid systems, the optimal control moves as well as the controlled switching times between two adjacent modes are determined online. In contrast to closely related schemes for integrated scheduling and control, the sequence of modes is not optimized. On the upper layer, the economic optimal control problem is solved rigorously by a slow hybrid economic model predictive controller at a low sampling rate. On the lower layer, a fast hybrid neighboring-extremal controller is based on the same economic optimal control problem as the slow controller to ensure consistency between both layers. The fast neighboring-extremal controller updates rather than tracks the optimal trajectories from the upper layer to account for disturbances. Consequently, the fast controller steers the process to its operational bounds under disturbances and the economic potential of the process is exploited anytime. The suggested two-layer control algorithm provides fully consistent control action on the fast and slow time-scale and thus avoids performance degradation and even infeasibilities which are commonly encountered if inconsistent optimal control problems are formulated and solved.

Toward an inherently safer design and operation of batch and semi-batch processes: The N-oxidation of alkylpyridines

This work shows an application of inherent safety principles to a reaction widely used in the pharmaceutical industry. More specifically, it incorporates the teachings of Trevor Kletz into the design of an inherently safer process for the N-oxidation of alkylpyridines. This reaction is of interest because of the hazards resulting from the undesired, gas-generating decomposition of hydrogen peroxide, the oxidizing agent. The generation of oxygen, combined with the flammability of the alkylpyridines, represents a serious fire and explosion hazard for this process. The purpose of this paper is to demonstrate how an inherently safer process can be potentially achieved by designing improved reactors and by assessing conditions that reduce or eliminate the hazards. Furthermore, it is shown that such improvement in safety increases the efficiency of the process and results in a cost reduction.

Hierarchical Economic NMPC for a Class of Hybrid Systems Using Neighboring-Extremal Updates

A two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems comprising important applications such as the economically optimal operation of recipe-driven batch or continuous processes. On the upper layer, the economic optimal control problem is solved rigorously by a slow controller at a low sampling rate, whereas a fast neighboring-extremal controller updates rather than tracks the optimal trajectories to account for disturbances. Consequently, the process is steered to its operational bounds, while the optimal switching times under disturbances can be determined such that the economic potential of the process is fully exploited anytime.

Optimal Batch Process Regulation using Self-optimizing Control, NCO tracking

Optimal operation of batch processes in the presence of uncertainty has been an area of significant research interest. The use of online measurements as a source of information offers scope to mitigate the effects of uncertainty and attain optimal operation. Therefore, amongst the different approaches proposed to address the uncertainty issues, measurement based optimization approaches have been shown to be eminently suited. Measurements have been broadly used in two approaches, viz. self optimizing control (SOC) and tracking of necessary conditions of optimality (NCO). In an earlier work for continuous processes by Jäschke and Skogestad [1], the relative complementary roles of NCO tracking and SOC were clearly highlighted in terms of their applicability in combating the effects of different scales of uncertainty and disturbances. For batch processes, the NCO tracking is found to be successful, the complementary role of SOC is largely un-explored. In this work, we extend the integrated approach of Jäschke and Skogestad [1] to batch processes by redefining the NCO and SOC layers. The proposed framework is evaluated using simulations involving a nonlinear batch polymerization of styrene.

Facile synthesis of SiO2–Au nanoshells in a three-stage microfluidic system

The capacity to produce nanostructured materials with well-defined, specific properties in relatively large quantities is crucial for the viability of emerging nanotechnologies. Core–shell plasmonic structures, such as SiO2–Au nanoshells, are a type of nanomaterial where the control of morphological properties is especially important, given the strong dependence of the plasmon frequency on their characteristic dimensions. Here we present a simple, robust and scalable process to exploit the advantages of microfluidics to manufacture these sophisticated nanostructures. We show that the main sequential steps required to obtain SiO2–Au nanoshells can be carried out in a simple microfluidic system with significant savings in time and effort, and with an improved control over the product properties, compared to a conventional batch processing operation.

Focus on vacuum and cryogenics

Focus on vacuum and cryogenics

Robust Data-Driven Modeling Approach for Real-Time Final Product Quality Prediction in Batch Process Operation

Making on-specification products is a primary goal, and also a challenge in chemical batch process operation. Due to the uncertainty of raw materials and instability of operating conditions, it may not produce the desired on-spec final product. It would be helpful if one can predict the product quality during each operation, so that one can make adjustments to process conditions in order to make on-spec product. This paper addresses the issue of real-time prediction of final product quality during a batch operation. First, a data-driven modeling approach is presented. This multimodel approach uses available process information up to the current points to capture their time-varying relationships with the final product quality during the course of operation, so that the prognosis of product quality can be obtained in real-time. Then, due to its data-driven nature, the focus is given on how to make the models robust in order to eliminate the effect of noise, especially, outliers in the data. A model-based outlier detection method is presented. The proposed approach is applied to a generic chemical batch case study, with its prediction performance being evaluated.

Dynamic Modeling and Optimal Control of Batch Reactors, Based on Structure Approaching Hybrid Neural Networks

A novel Structure Approaching Hybrid Neural Network (SAHNN) approach to model batch reactors is presented. The Virtual Supervisor−Artificial Immune Algorithm method is utilized for the training of SAHNN, especially for the batch processes with partial unmeasurable state variables. SAHNN involves the use of approximate mechanistic equations to characterize unmeasured state variables. Since the main interest in batch process operation is on the end-of-batch product quality, an extended integral square error control index based on the SAHNN model is applied to track the desired temperature profile of a batch process. This approach introduces model mismatches and unmeasured disturbances into the optimal control strategy and provides a feedback channel for control. The performance of robustness and antidisturbances of the control system are then enhanced. The simulation result indicates that the SAHNN model and model-based optimal control strategy of the batch process are effective.

A review on experimental studies of biosorption of heavy metals by Aspergillus niger

AbstractRecent developments in the field of environmental biotechnology lead to the search for adsorbents of biological origin, which is reasonably cheap for toxic trace metal removal. This method is specifically termed as biosorption. In this paper, biosorption of heavy metals by Aspergillus niger is reviewed. Characterisation of A. niger in heavy metal biosorption is extensively discussed. Batch and continuous processes are discussed in details. Influence of process parameters like biomass dosage rate, initial metal ion concentration, and pH of the solution, pretreatment, impact of co‐cations on metal biosorption is also discussed. Modelling for batch and continuous process operations are focused extensively. Mechanism of biosorption by A. niger is elaborated.

Development of reel-to-reel process system for roller-imprint on plastic fibers

We are developing a woven fabric of micro-electro-mechanical-systems (MEMS) where smart fibers are woven to make large-size flexible devices. MEMS structures and electric circuits are formed on the surface of individual fibers to serve as smart fibers where they can function as sensors and actuators. Moreover, when smart fibers are inter-woven it becomes necessary to process electric contact and physical positioning guides on the surfaces of warp and woof fibers. To transfer various patterns on the surface of a fiber at high speed, a batch process by thermal nanoimprinting is pursued. We then developed a new-type roller nanoimprint system to precisely transfer fine patterns from a plane mold onto the curved surface of a fibrous substrate. In this system, a fiber is sandwiched by two molds and rolls under the traction force of sliding molds traveling in opposite directions. At the end of their travel the molds are separated. The molds are moved in directions opposite to their previous directions of travel. This brings the molds back to their inital positions. Then, the fiber is moved by a preset distance using a reel-to-reel feeder. A new cycle starts again. With this method, 5-μm-width square and 5-μm-diameter circular dotted patterns with 10μm pitch were successfully transferred onto a 250-μm-diameter plastic optical fiber (POF) covering its full surface. Moreover, we succeeded in a continuous molding on the entire curved surface of 1.6m long POF using a reel-to-reel feeder as a batch processing operation with a pitch of 16mm by a repetition of roller-imprinting for 100 times. No significant difference was observed when the shapes and depths of the imprinted patterns obtained from the first imprinting were compared with those of obtained from the 100th imprinting.

Selective Hydrogenolysis of Glycerol to 1,2-Propanediol: Comparison of Batch and Continuous Process Operations

The screening of copper chromite catalysts with various promoters such as Al, Zn, and Ba for glycerol hydrogenolysis to 1,2-propanediol (1,2-PDO) in a batch reaction showed that Cu−Cr (Ba) catalyst gave the highest conversion of 34% and selectivity of 84% to 1,2-PDO. In a continuous operation (23 g catalyst bed) the same catalyst showed higher conversion of glycerol and selectivity for 1,2-PDO of 65% and >90%, respectively, with an on-stream activity of ∼800 h. Better performance in a continuous operation could be due to the in situ activation of the catalyst, suppression of glycerol cracking to ethylene glycol, as well as excessive hydrogenation of 1,2-PDO to 2-propanol due to lower contact time of 1.3 h as compared to that in a batch operation (5 h). Effects of various process parameters on conversion and selectivity also have been compared for batch and continuous operations.

Interrelation of Chemistry and Process Design in Biodiesel Manufacturing by Heterogeneous Catalysis

The pros and cons of using heterogeneous catalysis for biodiesel manufacturing are introduced, and explained from a chemistry and engineering viewpoint. Transesterification reactions of various feed types are then compared in batch and continuous process operation modes. The results show that the reaction chemistry and process kinetics characterising a particular feedstock are determinant factors for obtaining high-grade biodiesel. When using heterogeneous catalysis, the biodiesel quality of a particular feed can be controlled by customising the process design and operation conditions.

Model based control of a liquid swelling constrained batch reactor subject to recipe uncertainties

This work presents the application of nonlinear model predictive control (NMPC) to a simulated industrial batch reactor subject to safety constraint due to reactor level swelling, which can occur with relatively fast dynamics. Uncertainties in the implementation of recipes in batch process operation are of significant industrial relevance. The paper describes a novel control-relevant formulation of the excessive liquid rise problem for a two-phase batch reactor subject to recipe uncertainties. The control simulations are carried out using a dedicated NMPC and optimization software toolbox OptCon which implements efficient numerical algorithms. The open-loop optimal control problem is computed using the multiple-shooting technique and the arising nonlinear programming problem is solved using a sequential quadratic programming (SQP) algorithm tailored for large-scale problems, based on the freeware optimization environment HQP. The fast response of the NMPC controller is guaranteed by the initial value embedding and real-time iteration technologies. It is concluded that the OptCon implementation allows small sampling times and the controller is able to maintain safe and optimal operation conditions, with good control performance despite significant uncertainties in the implementation of the batch recipe.

Simultaneous optimal synthesis, design and operation of batch and continuous hybrid separation processes

A procedure for the optimal determination of process synthesis, design and operation for both batch and continuous separation is presented. The procedure allows the simultaneous determination of the optimal process from any specified available process alternatives, its configuration, its design and corresponding operation in order to perform a given separation duty. In this work, distillation, pervaporation and hybrid distillation/pervaporation processes are considered as process alternatives. The optimisation strategy takes into account an overall economic index that encompasses capital investment, operating costs and production revenues. Furthermore, rigorous mathematical models developed from first principles for distillation and pervaporation are used. Case studies are presented and it is found that hybrid distillation/pervaporation processes are the optimal synthesis solution in most cases and large costs savings can be made compared to traditional operation based on distillation alone.

P.6.a.008 Increased plasma brain-derived neurotrophic factor and nerve growth factor in the patients with alcohol dependence

A prototype system has been developed by integrating two higher-level hierarchical production planning application programs (aggregate production plan (APP), master production schedule (MPS)) using a common data model integration approach into an existing planning system for short-term scheduling and supervisory batch management which was originally proposed and developed by Richard, J. G., Macchietto, S. & Shah, N. (1999). Integrated decision support in flexible multipurpose plants. Computers & Chemical Engineering, 23, S539–S542. The hierarchical production planning system has been modelled and validated around an industrial scenario concerning multi-site, multipurpose batch process operations making household chemicals from start to transportation to regional warehouses. This preliminary work suggests that the idea of integrating software applications through a common data model, which was originally described by Stanley, G. M. (1994). The emerging trend towards knowledge-based frameworks for computer-integrated manufacturing. Advances in Instrumentation & Control, 49, 1121–1133 and further developed by Rickard, J. G., Macchietto, S. & Shah, N. (1999). Integrated decision support in flexible multipurpose plants. Computers & Chemical Engineering, 23, S539–S542 is feasible. However further research work, improvements and validations are required using varieties of industrial batch process operations and distribution problems to prove its viability.