Causal temporal graph attention network for fault diagnosis of chemical processes

Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.

Chemical etching process of copper electrode for bioelectrical impedance technology

In order to obtain bioelectrical impedance electrodes with high stability, the chemical etching process was used to fabricate the copper electrode with a series of surface microstructures. By changing the etching processing parameters, some comparison experiments were performed to reveal the influence of etching time, etching temperature, etching liquid concentration, and sample sizes on the etching rate and surface microstructures of copper electrode. The result shows that the etching rate is decreased with increasing etching time, and is increased with increasing etching temperature. Moreover, it is found that the sample size has little influence on the etching rate. After choosing the reasonable etching liquid composition （formulation 3）, the copper electrode with many surface microstructures can be obtained by chemical etching process at room temperature for 20 rain. In addition, using the alternating current impedance test of electrode-electrode for 24 h, the copper electrode with a series of surface microstructures fabricated by the etching process presents a more stable impedance value compared with the electrocardiograph （ECG） electrode, resulting from the reliable surface contact of copper electrode-electrode.

Mechanism of Formation of the Ozone Valley over the Tibetan Plateau in Summer-Transport and Chemical Process of Ozone

With the 3D chemical transport model OSLO CTM2, the valley of total column ozone over the Tibetan Plateau in summer is reproduced. The results show that when the ozone valley occurs and develops, the transport process plays the main part in the ozone reduction, but the chemical process partly compensates for the transport process. In the dynamic transport process of ozone, the horizontal transport process plays the main part in the ozone reduction in May, but brings about the ozone increase in June and July. The vertical advective process gradually takes the main role in the ozone reduction in June and July. The effect of convective activities rises gradually so that this effect cannot be overlooked in July, as its magnitude is comparable to that of the net changes. The effect of the gaseous chemical process brings about ozone increases which are more than the net changes sometimes, so the chemical effect is also important.

Chemical and biological flocculation process to treat municipal sewage and analysis of biological function

The pilot scale experimental apparatus and the procedure of the chemical and biological flocculation process to verify the feasibility in treating Shanghai municipal sewage were introduced in this paper. In addition, the biological function of the process was discussed. The results of optimal running showed that in the reaction tank, the concentration of mixed liquor suspended solid(MLSS) was 2 g/L, hydraulic retention time(HRT) was 35 min, dosage of liquid polyaluminium chloride(PAC) was 60 mg/L, and the concentration of polyacrylamide(PAM) was 0 5 mg/L. The effluent average concentrations of COD Cr , TP, SS and BOD 5 were 50 mg/L, 0 62 mg/L, 18 mg/L, and 17 mg/L, respectively. These were better than the designed demand. In addition, the existence of biological degradation in this system was proven by several methods. The removal efficiencies of the chemical and biological flocculation process were 20% higher than that of the chemical flocculation process above at the same coagulant dosage. The treatment process under different situations was evaluated on a pilot scale experiment, and the results provided magnificent parameters and optimal condition for future operation of the plant.

Multivariable Decoupling Predictive Control with Input Constraints and Its Application on Chemical Process

A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.

Chemical oxygen demand reduction in coffee wastewater through chemical flocculation and advanced oxidation processes

The removal of the natural organic matter present in coffee processing wastewater through chemical coagulation-flocculation and advanced oxidation processes （AOP） had been studied. The effectiveness of the removal of natural organic matter using commercial flocculants and UV/H202, UV/O3 and UV/H2O2/O3 processes was determined under acidic conditions. For each of these processes, different operational conditions were explored to optimize the treatment efficiency of the coffee wastewater. Coffee wastewater is characterized by a high chemical oxygen demand （COD） and low total suspended solids. The outcomes of coffee wastewater treatment using coagulation-flocculation and photodegradation processes were assessed in terms of reduction of COD, color, and turbidity. It was found that a reduction in COD of 67% could be realized when the coffee wastewater was treated by chemical coagulation-flocculation with lime and coagulant T-1. When coffee wastewater was treated by coagulation-flocculation in combination with UV/H2O2, a COD reduction of 86% was achieved, although only after prolonged UV irradiation. Of the three advanced oxidation processes considered, UV/H2O2, UV/O3 and UV/H2O2/O3, we found that the treatment with UV/H2O2/O3 was the most effective, with an efficiency of color, turbidity and further COD removal of 87%, when applied to the flocculated coffee wastewater.

Study on the Genetics and Development of Fiber Pigments and Color Deviation After Wetting Process of Naturally Colored Cotton

The genetic control of fiber pigment color in naturally colored cotton was studied. The expression of brown and green fiber color was controlled by incompletely dominant single genes and incompletely dominant major genes, respectively. Production and accumulation of the fiber pigment were related to special expression of enzymatic genes for pigment synthesis in fiber cells. At the stage of fiber lengthening, naturally colored cotton, like white cotton, appeared purely white. But when fiber cell walls entered the thickening stage, pigment appeared by degrees. When the fiber was completely matured (on boll dehiscence), the color reached its darkest level. After wetting process treatment, the hues of the fiber pigment changed in regular patterns. The hue circle for brown and green cotton changed in the opposite direction with wetting process treatment. In general, the treated cotton color and luster became dark and vivid, and this trend provided the possibility for enhancing the fiber quality by suitable environmental friendly finishing. The analysis showed that the color and luster of the cotton may be controlled by a series of pigments which show different chemical performance.

Petro-chemical wastewater treatment by biological process

In order to study the feasibility of treating petro chemical wastewater by the combination of anaerobic and aerobic biological process, a research of treating wastewater in UASB reactor and aeration basin has been conducted. The test results shows that under moderate temperature, with 5\^2 kgCOD/(m\+3·d) volumetric load of COD Cr in the UASB reactor and 24h of HRT, 85% removal rate of BOD 5 and 83% of COD \{Cr\} and 1\^34 m\+3/(m\+3·d) volumetric gas production rate can be obtained respectively. The aerobic bio degradability can be increased by 20%—30% after the petro chemical wastewater has been treated by anaerobic process. As Ns=0\^45 kgCOD/(kgMLSS·d), HRT=4h in the aeration tank, 94% removal rate of BOD 5, 93% of COD \{Cr\}, 98\^8% total removal rate of COD \{Cr\} and 99% removal rate of BOD 5 can be reached.

Modeling and Optimization for Scheduling of Chemical Batch Processes

Chemical batch processes have become significant in chemical manufacturing. In these processes, large numbers of chemical products are produced to satisfy human demands in daily life. Recently, economy globalization has resulted, in growing worldwide competitions in tradi.tional chemical .process industry. In order to keep competitive in the global marketplace, each company must optimize its production management and set up a reactive system for market fluctuation. Scheduling is the core of production management in chemical processes. The goal of this paper is to review the recent developments in this challenging area. Classifications of batch scheduling problems and optimization methods are introduced. A comparison of six typical models is shown in a general benchmark example from the literature. Finally, challenges and applications in future research are discussed.

Process simulation and optimization of flow field in wet electrostatic precipitator

To improve the dust removal performance of the wet electrostatic precipitator(WESP), a flow field optimization scheme was proposed via CFD simulation in different scales. The simplified models of perforated and collection plates were determined firstly. Then the model parameters for the resistance of perforated and collection plates, obtained by small-scale flow simulation, were validated by medium-scale experiments. Through the comparison of the resistance and velocity distribution between simulation results and experimental data, the simplified model is proved to present the resistance characteristics of perforated and collection plates accurately. Numerical results show that after optimization, both the flow rate and the pressure drop in the upper room of electric field regions are basically equivalent to those of the lower room, and the velocity distribution in flue inlet of WESP becomes more uniform. Through the application in practice, the effectiveness and reliability of the optimization scheme are proved, which can provide valuable reference for further optimization of WESP.

SDG-based Model Validation in Chemical Process Simulation

Signed direct graph (SDG) theory provides algorithms and methods that can be applied directly to chemical process modeling and analysis to validate simulation models, and is a basis for the development of a software environment that can automate the validation activity. This paper is concentrated on the pretreatment of the model validation. We use the validation scenarios and standard sequences generated by well-established SDG model to validate the trends fitted from the simulation model. The results are helpful to find potential problems, assess possible bugs in the simulation model and solve the problem effectively. A case study on a simulation model of boiler is presented to demonstrate the effectiveness of this method.

Integrated Assessment of Environmental and Economic Performance of Chemical Products Using Analytic Hierarchy Process Approach

With the increasing public consciousness on environmental issues, chemical products and process designs require simultaneous satisfaction and compromise of environmental and economical requirements. To fulfill the two conflicting while complementary objectives, a systematic approach for life cycle design of a chemical product is proposed in this article. Multiattribute decision-making is adopted in a trade-off consideration of both technical economical evaluation and environmental impacts assessment using the analytic hierarchy process （AHP） approach. On the basis of an evaluation of the relative importance of the criteria multicriteria decision making is performed. In this study, an AHP model is used to derive single a criteria score by analyzing the environmental impact and life cycle cost of a product, respectively. And a fluctuant weight analysis is put forth to calculate the integrated index of the product to enable products to be ranked or selected intuitionally and conveniently. The proposed AHP model has been applied to a case study, a comparative study on chamber cleaning with NF3 and C2F6. The resuits show that the protposed AHP model is Capable of providing a rational and relevant evaluation.

Computational Mass Transfer Method for Chemical Process Simulation

The recent works on the development of computational mass transfer （CMT） method and its applications in chemical process simulation are reviewed. Some development strategies and challenges in future research are also discussed.

Microwave enhanced chemical reduction process for nitrite-containing wastewater treatment using sulfaminic acid

High-concentration nitrite-containing wastewater that presents extreme toxicity to human health and organisms is difficult to be treated using traditional biological process. In this study, a novel microwave-enhanced chemical reduction process （MECRP） using sulfarninic acid （SA） was proposed as a new manner to treat such type of wastewater. Based on lab-scale experiments, it was shown that 75%-80% nitrite （NO2-） could be removed within time as short as 4 min under 50 W microwave irradiation in pH range 5-10 when molar ratio of SA to nitrite （SA/NO2-） was 0.8. Pilot-scale investigations demonstrated that MECRP was able to achieve nitrite and chemical oxygen demand （COD） removal with efficiency up to 80% and 20%, respectively under operating conditions of SA concentration 80 kg/m3, SA/NO2- ratio 0.8, microwave power 3.4 kW, and stirring time 3 min. Five-day biological oxygen demand （BODs）/COD value of treated effluent after MECRP was increased from 0.05 to 0.36 （by 620%）, which clearly suggested a considerable improvement of biodegradability for subsequent biological treatment. This study provided a demonstration of using microwave irradiation to enhance reaction between SA and nitrite in a short time, in which nitrite in wastewater was completely converted into nitrogen gas without leaving any sludge and secondary pollutants.

Preparation of calcium stannate by modified wet chemical method

A modified wet chemical route for low-temperature synthesis of the calcium stannate CaSnO3, a potentialmaterial for dielectric applications is reported. Firstly, a precursor CaSn(OH)6 was prepared using tin tetrachloride,calcium chloride and sodium hydroxide at room temperature. Then the precursor was annealed at relatively low tem-perature of 600 ℃ to obtain CaSnO3. The phase identification, thermal behavior and surface morphology of the sam-ples were characterized by element analysis, X-ray diffraction (XRD), thermo-gravimetric (TG) analysis and deriva-tive thermo-gravimetric (DTG) analysis, Fourier transform infrared spectroscopy (FTIR) and scanning electron mi-croscopy (SEM) in detail. The results show that CaSnO3 obtained by this method possesses a cubic perovskitestructure with average grain size of 5 μm.

Simulation of Wetting and Drying Processes in A Depth Integrated Shallow Water Flow Model by Slot Method

A particular porosity method named ＂slot method＂ is implemented in a depth-integrated shallow water flow model （DIVAST） to simulate wetting and drying processes. Discussed is the relationship between the shape factors of the ＂slot＂ and the preset depth used in ＂wetting-drying＂ algorithm. Two typical tests are conducted to examine the performance of the method with the effect of the shape factors of the ＂slot＂ being checked in detail in the first test. Numerical results demonstrate that： 1 ） no additional effort to improve the finite difference scheme is needed to implement ＂slot method＂ in DIVAST, and 2） ＂slot method＂ will simulate wetting and diying processes correctly if the shape factors of the ＂slot＂ being selected properly.

A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes

The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.

Dimensionality Reduction with Input Training Neural Network and Its Application in Chemical Process Modelling

Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.

A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design

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 maximized 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 a generic 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 case study based on the design of a condensate treatment unit in an ammonia plant.

PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis(PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level(normally one alarm per minute). Finally,variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.