Volume 2, Issue 1

In electrochemical leaching processes the solid must be an electric conductor, e.g., a metal, or a semiconductor, e.g., certain metal sulfides or oxides. Reaction takes place by the transfer of electrons at the solid surface and involves oxidation–reduction processes that take place simultaneously at two different locations not far from each other. At one location, electrons are picked up by a depolarizer, D, in solution, e.g., O2, H+, etc. (the cathodic zone) and at another location metal ions are released in solution (the anodic zone) where they react with reagent C. A single kinetic law derived theoretically is obeyed: Rate = k1k2A[D][C] / (k1[D] + k2[C]) where k1 and k2 are constants, and A is the total surface area of the dissolving solid.

Reactive separations combining mass transfer with simultaneous chemical reactions within a single column unit provide an important synergistic effect and bring about several advantages. The influence of column internals in reactive separations increases significantly, because these internals have to enhance both separation and reaction and maintain a sound balance between them. To solve this problem, a novel generation of column internals with enhanced mass transfer performance and low pressure drop has been created. Among them, corrugated packings of the regular type or structured packings have gained a wide acceptance.This paper gives a state-of-the-art review of the structured packings modeling methods, focusing on two innovative and particularly promising approaches. The first of them is based on the application of CFD, whereas the second one employs the idea of hydrodynamic analogy between complex and simple flow patterns. Both approaches are illustrated with several case studies.

Extensive progress has been made in using CFD tool in the simulation of multiphase flows in some gravity concentrators. Several case studies for calculation of multiphase flows by different numerical models in spiral, dense medium cyclone, water only cyclones, hindered-settling bed separator, heavy medium vessel and jig are reviewed. The Euler-Lagrange approach in calculation of the particle movement and particle-liquid coupling effect are also discussed. The limitation of Euler-Euler models in the treatment of the particles with a size distribution, and disadvantages of discrete element method (DEM) in description of the jigging processes will be presented. The successful two-dimensional simulation of the hindered-settling bed separator, heavy medium vessel and jig by Euler-Lagrange approach is also addressed.

The production of cyclotol, a military high explosive used in manufacture of aerial bombs, involves the multiphase mixing of molten TNT, RDX solid particles and water. Textural homogeneity of the final cyclotol mixture and near-zero water composition are critical requirements to minimize bomb-rejection rate and hence improved plant profitability. Although current data reveal significant statistical variation in product quality, detailed investigation of the complex mixing operation does not lend itself to physical experimentation due to the explosive nature of the process. This forestalls opportunities for improved process operation or re-design. To circumvent this constraint, a computational fluid dynamics (CFD) simulation of the industrial-scale process was carried out to understand how current mixing protocols impact on key in-tank profiles such as water draw-down, RDX particles suspension, turbulence kinetic energy, velocity magnitude, vector direction and water-entrainment. The analysis revealed that while the maximum impeller speed allowable to avoid water draw-down is 20 rpm, the minimum speed necessary to suspend the RDX particles for homogeneous mixing is 40 rpm. These opposing requirements are neither accommodated by the present mixing protocols nor achievable with the existing tank design. Even so, the 'mystery' of irrational cyclotol quality has been uncovered by a judicious use of CFD modeling and opened up the possibility of a revised tank design and optimal process operation for superior plant economics.

The performance analysis of three advanced non linear controllers is the main focus of this paper. All three controllers are applied for the control of a batch polymerisation reactor which is defined by a very simple kinetic model for the polymerisation of styrene. This simple set of equations describing the polymerisation process is first solved using the sequential strategy i.e. Control Vector Parameterisation (CVP) technique within gPROMS to find optimal initial initiator concentrations and the reactor temperature trajectory necessary to yield desired polymer molecular properties (defined here as fixed values of monomer conversion and number average chain length) in minimum time. The sequential solution strategy has had limited application in solving optimisation problems for polymerisation in batch reactors, most researchers instead employing the Pontryagin's Maximum Principle (PMP) to solve optimal control problems involving these systems.The temperature trajectory obtained from the dynamic optimisation is used as the setpoint to be tracked by the three controllers: Dual Mode control with PID, which is representative of industrial practice, Generic Model Control (GMC) with Neural Networks as online heat release estimator, and Direct Inverse Control (DIC). Published work on the last two controllers as applied to the control of a batch polymerisation reactor is absent from the literature.When the performances of the different controllers are evaluated, it is seen that the GMC-NN controller performs better than the other two for the system under consideration.

Improvement in accuracy without sacrificing stability and convergence of the solution to unsteady diffusion heat transfer problems by computational method of enhanced explicit scheme (EES), has been achieved and demonstrated, through transient one dimensional and two dimensional heat conduction. The truncation error induced in the explicit scheme using finite difference technique is eliminated by optimization of partial derivatives in the Taylor series expansion, by application of interface theory developed by the authors. This theory, in its simple terms gives the optimum values to the decision vectors in a redundant linear equation. The time derivatives and the spatial partial derivatives in the transient heat conduction, take the values depending on the time step chosen and grid size assumed. The time correction factor and the space correction factor defined by step sizes govern the accuracy, stability and convergence of EES. The comparison of the results of EES with analytical results, show decreased error as compared to the result of explicit scheme. The paper has an objective of reducing error in the explicit scheme by elimination of truncation error introduced by neglecting the higher order terms in the expansion of the governing function. As the pilot examples of the exercise, the implementation is aimed at solving one-dimensional and two-dimensional problems of transient heat conduction and compared with the results cited in the referred literature.

A steady-state cross-flow reactor for ethylene epoxidation was simulated using industrial data (low oxygen/ethylene feed ratio) and pilot plant data (high oxygen/ethylene feed ratio). A comparison between the conventional fixed-bed reactor and the cross-flow reactor with two types of feeding modes was performed. The present study showed the possibility of enhancing the reactor yield by distributing the ethylene optimally at equal spaced along the reactor for both commercial and pilot plant cases. We have shown that the evenly distributed feeding mode does not increase the desired ethylene oxide yield. The model results confirmed that the cross-flow reactor with ethylene is the best configuration, followed by the conventional fixed-bed reactor and the cross-flow reactor with oxygen, respectively.

This article presents a solution to pH control based on model-free learning control (MFLC). The MFLC technique is proposed because the algorithm gives a general solution for acid-base systems, yet is simple enough for implementation in existing control hardware. MFLC is based on reinforcement learning (RL), which is learning by direct interaction with the environment. The MFLC algorithm is model free and satisfying incremental control, input and output constraints. A novel solution of MFLC using multi-step actions (MSA) is presented: actions on multiple time scales consist of several identical primitive actions. This solves the problem of determining a suitable fixed time scale to select control actions so as to trade off accuracy in control against learning complexity. An application of MFLC to a pH process at laboratory scale is presented, showing that the proposed MFLC learns to control adequately the neutralization process, and maintain the process in the goal band. Also, the MFLC controller smoothly manipulates the control signal.

This paper attempts to address a common difficulty encountered when trying to justify a process control revamp or upgrading project — the ability to quantify potential savings/profits. A relatively simple procedure that is applicable to both continuous and digital controllers is described. Given models of the process and the controller, knowledge of the disturbance frequencies and their variances, the method is able to predict accurately the variance of the controlled output of linear control systems.

Hydrodynamics study of a continuous bubble chain rising through liquid column has been performed for laboratory scale bubble column using the volume-of-fluid (VOF) approach. The effect of operating and design parameters on the bubble size distribution and rise trajectory has been investigated for air-water system. For the same distributor, simulation results have indicated the formation of small bubbles at low superficial gas velocity and relatively large bubbles at higher velocities. The increase in the hole-size of distributor has shown similar behaviour. Analysis of bubble trajectories for different superficial gas velocities and distributors has demonstrated an oscillatory behaviour exhibited by small bubbles formed at low superficial gas velocity. A reasonable agreement between the predicted values of gas hold-up with the experimental work has validated the present model.

This article demonstrates, using simulations, the potential of the S-system formalism for the inference of unknown chemical reaction networks from simple experimental data, such as that typically obtained from laboratory scale reaction vessels. Virtually no prior knowledge of the products and reactants is assumed. S-systems are a power law formalism for the canonical approximate representation of dynamic non-linear systems. This formalism has the useful property that the structure of a network is dictated only by the values of the power law parameters. This means that network inference problems (e.g. inference of the topology of a chemical reaction network) can be recast as parameter estimation problems. The use of S-systems for network inference from data has been reported in a number of biological fields, including metabolic pathway analysis and the inference of gene regulatory networks. Here, the methodology is adapted for use as a hybrid modelling tool to facilitate the reverse engineering of chemical reaction networks using time series concentration data from fed-batch reactor experiments. The principle of the approach is demonstrated with noisy simulated data from fed-batch reactor experiments using a hypothetical reaction network comprising 5 chemical species involved in 4 parallel reactions. A co-evolutionary algorithm is employed to evolve the structure and the parameter values of the S-system equations concurrently. The S-system equations are then interpreted in order to construct a network diagram that accurately reflects the underlying chemical reaction network.

The manuscript is focused on the optimization of a batch distillation column that is actually formulated in terms of control problem. The final objective is finding the optimal time-dependent reflux profile. In order to fulfill the call for simplicity and functionality from the industries, the reflux profile was approximated by means of a piece-wise function with three time intervals. A first-principles model, derived from the literature, proved to be a good trade-off between computational effort and accuracy. The capacity factor (CAP), defined as the on-specification products produced per time unit, is the performance index that must be maximized. The optimization problem was solved by a specifically modified Simplex method that demonstrated a good behavior in case of discontinuous objective function. Finally, a graphical analysis of the capacity factor was performed to show the irregularities of such a function.

Like many globalized industries, the pulp and paper sector finds itself with an increasingly demanding clientele, who continually expect a better and cheaper product. An important design strategy being employed to address this objective is through an analysis of the vast quantity of process and product data accumulated in plant-wide data historians, in order to improve operations. Mill processes are multivariate, meaning that the interactions between the variables are as important as the variables themselves. Process relationships must therefore be modeled as a group, using an appropriate simulation technique like Multivariate Analysis (MVA), with suitable data pre-processing to account for process upsets and other disturbances. In a previous paper, using an Eastern Canadian newsprint mill as an industrial case study, we showed that it was possible to find statistically significant correlations between wood chip refiner operation, intermediate pulp quality, and final paper quality using data-driven models. This was true even though some important process parameters went unmeasured, process lags changed with time, and the operation of key equipment items changed gradually with use. The present study compares the use of different timescales and combinations of unit operations to determine which ones yield the best MVA simulations. Because plant operating data were used, and experimental design was not practical, it is possible that some of the correlations found could be attributable to coincidence. We therefore added and removed variables and time periods to explore the validity of the models. The best MVA models were obtained by using a shorter (1-hour) data timescale, although use of a weighted-average filter helped to bridge the gap between these faster readings and the slower paper quality trends.