A new design method for a water-reusing network, with a hybrid structure, to reduce the complexity of the network and to minimize freshwater consumption, is proposed. The unique feature of the methodology proposed in this article is to control the complexity of the water network by regulation of the control number in a water-reusing system. It combines the advantages of a conventional water-reusing network and a water-reusing network with internal water mains. To illustrate the proposed method, a single contaminant system and a multiple contaminant system serve as examples of the problems.

The problem of optimal synthesis of an integrated water system is addressed in this study, where water using processes and water treatment operations are combined into a single network such that the total cost of fresh water and wastewater treatment is globally minimized. A superstructure that incorporates all feasible design alterna-tives for wastewater treatment, reuse and recycle, is synthesized with a non-linear programming model. An evolutionary approach—an improved particle swarm optimization is proposed for optimizing such systems. Two simple examples are presented to illustrate the global optimization of integrated water networks using the proposed algorithm.

This article deals with the design of energy efficient water utilization systems allowing operation split. Practical features such as operating flexibility and capital cost have made the number of sub operations an important parameter of the problem. By treating the direct and indirect heat transfers separately, target freshwater and energy consumption as well as the operation split conditions are first obtained. Subsequently, a mixed integer non-linear programming (MINLP) model is established for the design of water network and the heat exchanger network (HEN). The proposed systematic approach is limited to a single contaminant. Example from literature is used to illustrate the applicability of the approach.

Method for constructing the optimal water supply line and formulas for calculating the targets for single-contaminant regeneration recycling water systems are improved to apply to the situation of variational pa-rameters in this article. Based on these extending methods, the effect of varying freshwater consumption and regen-erated water flow rate on the optimizing results are investigated. The interactions of parameters of regeneration recycling systems are summarized. Finally, all the conclusions are illustrated from the results of mathematical programming through an example.

It is the fact that several process parameters are either unknown or uncertain. Therefore, an optimal control profile calculated with developed process models with respect to such process parameters may not give an optimal performance when implemented to real processes. This study proposes a batch-to-batch optimization strategy for the estimation of uncertain kinetic parameters in a batch crystallization process of potassium sulfate production. The knowledge of a crystal size distribution of the product at the end of batch operation is used in the proposed methodology. The updated kinetic parameters are applied for determining an optimal operating temperature policy for the next batch run.

This article presents a simulated annealing-based approach to the optimal synthesis of distillation column considering intermediate heat exchangers arrangements. The number of intermediate condensers and/or inter-mediate reboilers, the placement locations, the operating pressure of column, and the heat duties of intermediate heat exchangers are treated as optimization variables. A novel coding procedure making use of an integer number series is proposed to represent and manipulate the structure of system and a stage-to-stage method is used for column design and cost calculation. With the representation procedure, the synthesis problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which can then be solved with an improved simulated annealing algorithm. Two examples are illustrated to show the effectiveness of the suggested approach.

Liquid-phase oxidation of toluene with air has become the main technology for producing benzoic acid in a reactor at present. Based on the kinetic model of the toluene oxidation process obtained from laboratory and mass balance of key component, a novel model is established to simulate the industrial toluene oxidation process, in which the effects of benzaldehyde and benzyl alcohol are considered and the kinetic parameters are revised by industrial data. The simulation results show that the error of benzoic acid yield is within 3.5%. Based on the simulation model, to maximize the benzoic acid yield, an optimization model is proposed to optimize the operating parameters, including toluene feed-in mass flux and temperature. The optimization result indicates that on the allow-able operating conditions, the maximum benzoic acid yield obtained with the reaction temperature at 167.2℃ and the mass flux at 104.1 t·h^-1 is greater than the current one, which can be used to guide industrial reactor’s operation.

In many circumstances, chemical process design can be formulated as a multi-objective optimization(MOO) problem. Examples include bi-objective optimization problems, where the economic objective is maxi-mized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process.Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose ageneric and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a casestudy based on the design of a condensate treatment unit in an ammonia plant.

Today’s automation industry is driven by the need for an increased productivity, higher flexibility, andhigher individuality, and characterized by tailor-made and more complex control solutions. In the processing industry, logic controller design is often a manual, experience-based, and thus an error-prone procedure. Typically, thespecifications are given by a set of informal requirements and a technical flowchart and both are used to be directlytranslated into the control code. This paper proposes a method in which the control program is constructed as a sequential function chart (SFC) by transforming the requirements via clearly defined intermediate formats. For thepurpose of analysis, the resulting SFC can be translated algorithmically into timed automata. A rigorous verificationcan be used to determine whether all specifications are satisfied if a formal model of the plant is available which isthen composed with the automata model of the logic controller (LC).

A new on-line predictive monitoring and prediction model for periodic biological processes is proposedusing the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subse-quently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatmentprocess, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which isthus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.

Digraph-based causal models have been widely used to model the cause and effect behavior of processsystems. Signed digraphs (SDG) capture the direction of the effect. It should be mentioned that there are loops in SDG generated from chemical process. From the point of the inherent operability, the worst unsafe factor is the SDG having positive loops that means any disturbance occurring within the loop will propagate through the nodesone by one and are amplified gradually, so the system may lose control, which may lead to an accident. So findingthe positive loops in a SDG and treating these unsafe factors in a proper manner can improve the inherent safety of a chemical process. This article proposed a method that can detect the above-mentioned unsafe factors in the process conceptual design stage automatically through the analysis of the SDG generated from the chemical process. Acase study is illustrated to show the working of the algorithm, and then a complicated case from industry is studiedto depict the effectiveness of the proposed algorithm.

Integration amongst various decision-making processes, such as planning, design, and operation is necessary to dynamic and flexible batch production. To achieve a batch production integration, utilization of commonmodels used for various decision-making processes is an effective approach. From this point of view, a batch system common model as described by a Petri net is proposed. In this article, a fault diagnosis technique for batchprocesses is presented using information about fault propagation and the possibilities of integration of fault analysisand controller synthesis are discussed on the basis of the Petri net based common models.

The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using Neur On-Line, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment, and a new modified Broyden, Fletcher, Goldfarb, and Shanno (BFGS) quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of back propagation (BP) neural networks is developed and embedded into Neur On-Line by introducing a new search method of learning rate to the full memory BFGS algorithm. Simulation results show that the adaptive learning and prediction neural network system can quickly track the time-varying and nonlinear behavior of the bioreactor.

This article addresses a production planning optimization problem of overall refinery. The authors formulated the optimization problem as mixed integer linear programming. The model considers the main factors for optimizing the production plan of overall refinery related to the use of run-modes of processing units. The aim of this planning is to decide which run-mode to use in each processing unit in each period of a given horizon, to satisfy the demand, such as the total cost of production and inventory is minimized. The resulting model can be regarded as a generalized lot-sizing problem where a run-mode can produce and consume more than one product. The resulting optimization problem is large-sized and NP-hard. The authors have proposed a column generation-based algorithm called branch-and-price (BP) for solving the interested optimization problem. The model and implementation of the algorithm are described in detail in this article. The computational results verify the effectiveness of the proposed model and the solution method.

The management and control of material flow forms the core of manufacturing execution systems (MES) in the petrochemical industry. The bottleneck in the application of MES is the ability to match the material-flow model with the production processes. A dynamic material-flow model is proposed in this paper after an analysis of the material-flow characteristics of the production process in a petrochemical industry. The main material-flow events are described, including the movement, storage, shifting, recycling, and elimination of the materials. The spatial and temporal characters of the material-flow events are described, and the material-flow model is constructed. The dynamic material-flow model introduced herein is the basis for other subsystems in the MES. In addition, it is the subsystem with the least scale in MES. The dynamic-modeling method of material flow has been applied in the development of the Sino MES model. It helps the petrochemical plant to manage the entire flow information related to tanks and equipments from the aspects of measurement, storage, movement, and the remaining balance of the material. As a result, it matches the production process by error elimination and data reconciliation. In addition, it facilitates the integration of application modules into the MES and guarantees the potential development of SinoMES in future applications.

To provide a theoretical basis for optimizing the pervaporation procedure, a mass transfer model for pervaporation for binary mixtures was developed based on the multi-fields synergy theory. This model used the mechanism of sorption-diffusion-desorption and introduced a diffusion coefficient, which was dependent on the feed concentration and temperature. Regarding the strong coupling effect in the mass transfer, the concentration distribution in membrane was predicted using the Flory-Huggins thermodynamic theory. The batch experiments and other experiments with constant composition were conducted using a modified chitosan pervaporation membrane to separate tert-butyl alcohol (TBA)-water mixtures. The parameters of the mass transfer model were obtained from the flux of the experiments with a constant composition and the activity coefficients available through phase equi-librium equation, using the Willson equation in the feed side and the Flory-Huggins thermodynamic theory within the membrane. The simulation results of the experiments are in good agreement with the results of the experiments.

In this study, the developments in modeling gas-phase catalyzed olefin polymerization fluidized-bed reactors (FBR) using Ziegler-Natta catalyst is presented. The modified mathematical model to account for mass and heat transfer between the solid particles and the surrounding gas in the emulsion phase is developed in this work to include site activation reaction. This model developed in the present study is subsequently compared with well-known models, namely, the bubble-growth, well-mixed and the constant bubble size models for porous and non porous catalyst. The results we obtained from the model was very close to the constant bubble size model, well-mixed model and bubble growth model at the beginning of the reaction but its overall behavior changed and is closer to the well-mixed model compared with the bubble growth model and constant bubble size model after half an hour of operation. Neural-network based predictive controller are implemented to control the system and com-pared with the conventional PID controller, giving acceptable results.

This study focuses on automatic searching and verifying methods for the reachability, transition logics and hierarchical structure in all possible paths of biological processes using model checking. The automatic search and verification for alternative paths within complex and large networks in biological process can provide a consid-erable amount of solutions, which is difficult to handle manually. Model checking is an automatic method for veri-fying if a circuit or a condition, expressed as a concurrent transition system, satisfies a set of properties expressed in a temporal logic, such as computational tree logic (CTL). This article represents that model checking is feasible in biochemical network verification and it shows certain advantages over simulation for querying and searching of special behavioral properties in biochemical processes.

Two novel thermal cycles based on Brayton cycle and Rankine cycle are proposed, respectively, which integrate the recovery of low-level waste heat and Liquefied Nature Gas (LNG) cold energy utilization for power generation. Cascade utilization of energy is realized in the two thermal cycles, where low-level waste heat, low-temperature exergy and pressure exergy of LNG are utilized efficiently through the system synthesis. The simulations are carried out using the commercial Aspen Plus 10.2, and the results are analyzed. Compared with the conventional Brayton cycle and Rankine cycle, the two novel cycles bring 60.94% and 60% in exergy efficiency, respectively and 53.08% and 52.31% in thermal efficiency, respectively.

In this study, a reactive distillation column in which chemical reaction and separation occur simultane-ously is applied for the synthesis of tert-amyl ethyl ether (TAEE) from ethanol (EtOH) and tert-amyl alcohol (TAA). Pervaporation, an efficient membrane separation technique, is integrated with the reactive distillation for enhancing the efficiency of TAEE production. A user-defined Fortran subroutine of a pervaporation unit is developed, allowing the design and simulation of the hybrid process of reactive distillation and pervaporation in Aspen Plus simulator. The performance of such a hybrid process is analyzed and the results indicate that the integration of the reactive distillation with the pervaporation increases the conversion of TAA and the purity of TAEE product, compared with the conventional reactive distillation.

One of the most common problems arising from the application of exergy analysis is the allocation of cumulative exergy consumption (CExC) in the petroleum distillation process yielding several useful products. Based on the concept of exergy, an improved calculation of the minimum separation power of product (MSPP) in the petroleum distillation process is provided in this article. The calculation of MSPP can be derived from the concept of exergy. The related mathematical models are established. Finally, application of this method to a case study is given, and the results are compared with the ones using mass as an allocation parameter.

This article presents a multiscale simulation approach starting at the molecular level for the adsorption process development. A grand canonical Monte Carlo method is used for the prediction of adsorption isotherms of methanol on an activated carbon at the molecular level. The adsorption isotherms obtained in the linear region (or adsorption constant) are exploited as a model parameter required for the adsorption process simulation. The adsorption process model described by a set of partial differential equations (PDEs) is solved by using the conservation element and solution element method, which produces a fast and an accurate numerical solution to PDEs. The simulation results obtained from the adsorption constant estimated at the molecular level are in good agreement with the experimental results of the pulse response. The systematical multiscale simulation approach addressed in this study may be useful to accelerate the adsorption process development by reducing the number of experiments.

Activated carbon has been proven to be an effective adsorbent for the recovery of a wide variety of metal ions from aqueous solutions. In this research, the activated hard shell of Iranian apricot stones was used for gold recovery from electro-plating wastewater. The effect of parameters such as dose and particle size of adsorbent, pH, agitation speed of mixing on the gold recovery was investigated. The results showed that under the optimum operating conditions more than 98% of gold ions were adsorbed onto activated carbon after just 3 h. In addition, the adsorbed gold could be eluted from this adsorbent by improved striping method. The process involves contact of gold-laden adsorbent with a strong base at ambient temperatures followed by elution with an aqueous solution containing an organic solvent. It was found that activated hard shell of apricot stones has the potential to replace imported commercial activated carbons in gold adsorption processes.

Polyurea microcapsules containing NiCl₂ were prepared by interfacial polymerization between diisocy-anate and water with triethylamine as a catalyst in water-in-oil emulsion system. The influence of preparation conditions such as the dosage and feed mode of the catalyst, concentration of the encapsulated NiCl₂, and concentration and structure of diisocyanates on the breakage of the microcapsules have been evaluated. The results show that breakage is strongly dependent on the rate of polymerization and stability of initial emulsion. The improved micro-capsules with low breakage have been produced under the optimum conditions. Furthermore, the obtained micro-capsules immobilizing NiCl₂ as a recyclable catalyst is successfully used in benzaldehyde reduction.

Ketalization of catechol was studied with various carbonyl compounds using metal p-toluenesulfonate as biphasic catalysts. The results showed that copper p-toluenesulfonate was the most efficient catalysts for the reaction. The advantages of high activity, stability, reusability and low cost for the simple synthetic procedure made the catalyst one of the best choice for the reaction.

An iterative optimization strategy is proposed and applied to the steady state optimizing control of the bio-dissimilation process of glycerol to 1,3-propanediol in the presence of model-plant mismatch and input constraints. The scheme is based on the Augmented Integrated System Optimization and Parameter Estimation (AI-SOPE) technique, but a linearization of some performance function in the modified model-based optimization problem of AISOPE is introduced to overcome the difficulty of determining an appropriate penalty parameter. When carrying out the iterative optimization, the penalty coefficient is set to a larger value at the current iteration than at the previous iteration, which can promote the evolution rate of the iterative optimization. Simulation studies illustrate the potential of the approach presented for the optimizing control of the bio-dissimilation process of glyc-erol to 1,3-propanediol. The effects of measurement noise, measured and unmeasured disturbances on the proposed algorithm are also investigated.

Elementary flux mode (EFM) analysis was used in the metabolic analysis of central carbon metabolism in Saccharomyces cerevisiae based on constructed cellular network. Calculated from the metabolic model, the ethanol-producing pathway No. 37 furthest converts the substrate into ethanol among the 78 elementary flux modes. The in silico metabolic phenotypes predicted based on this analysis fit well with the fermentation performance of the engineered strains, KAM3 and KAM11, which confirmed that EFM analysis is valid to direct the construction of Saccharomyces cerevisiae engineered strains, to increase the ethanol yield.

A novel anaerobic reactor, jet biogas inter-loop anaerobic fluidized bed (JBILAFB), was designed and constructed. The start-up and performance of the reactor was investigated in the process of artificial glucose waste-water treatment. With the wastewater recycle ratio of 2.5:1, the recycled wastewater with biogas could mix sludge and wastewater in the JBILAFB reactor completely. The start-up of the JBILAFB reactor could be completed in less than 70 d through maintenance of hydraulic retention time (HRT) and stepwise increase of feed total organic carbon (TOC) concentration. After the start-up, with the volumetric TOC loadings of 14.3 kg·m^-3·d^-1, the TOC removal ratio, the effluent pH, and the volatile fatty acids (VFA)/alkalinity of the JBILAFB reactor were more than 80%, close to 7.0 and less than 0.4, respectively. Moreover, CH₄ was produced at more than 70% of the theoretical value. The reactor exhibited high stability under the condition of high volumetric TOC loading. Sludge granules in the JBI-LAFB reactor were developed during the start-up and their sizes were enlarged with the stepwise increase of volu-metric TOC loadings from 0.8 kg·m^-3·d^-1 to 14.3 kg·m^-3·d^-1. Granules, an offwhite color and a similar spherical shape, were mainly comprised of global-like bacteria. These had good methanogenic activity and settleability, which were formed probably through adhesion of the bacteria. Some inorganic metal compounds such as Fe, Ca, Mg, Al, etc. were advantageous to the formation of the granules.

The catalytic performance of some quaternary ammonium salts for the liquid phase reaction of butanol and hydrochloric acid at different conditions was studied experimentally and compared with the traditional catalyst (ZnCl₂). The organic ammonium catalysts investigated include ionic liquids N-butyl-N-methyl imidazolium fluobo-rate ([BMIM][BF₄]) and N-butyl-N-methylimidazolium chloride ([BMIM]Cl) as well as hydrochloric salts of N-methylimidazol ([HMIM]Cl), pyridine ([HPy]Cl) and triethylamine ([HEt₃N]Cl). It is shown that the intrinsic catalytic performance of all organic ammonium salts except [HEt₃N]Cl is slightly superior to ZnCl₂, while the selectivity of butyl chloride is nearly at the same level around 96%. The conversion of butanol increases slightly with temperature and the catalyst amount added while the variation of selectivity is not obvious. Based on the recycle experiments, the ionic liquids as catalyst for the reaction of butanol and hydrochloric acid can be used more than 5 times, which suggests great potential of using ionic liquids as novel catalyst for such reactions.

Waste reduction is gaining importance as the preferred means of pollution prevention. Reactor network synthesis is one of the key parts of chemical process synthesis. In this study, a geometric approach to reactor net-work synthesis for waste reduction is presented. The bases of the approach are potential environment impact (PEI) rate-law expression, PEI balance and the instantaneous value of environmental indexes. The instantaneous value can be derived using the PEI balance, PEI rate-law expression and the environmental indexes. The optimal reactor networks with the minimum generation of potential environment impact are geometrically derived by comparing with areas of the corresponding regions. From the case study involving complex reactions, the approach does not involve solving the complicated mathematical problem and can avoid the dimension limitation in the attainable region approach.