Advanced process control system with MPC as a new approach for layer of protection analysis

Layer of protection analysis (LOPA) is a widely used method to support process safety in the chemical industries. In the LOPA, the process is classified into many layers, one of such layers considers the basic process control system (BPCS) which commonly uses PID controllers. This kind of controllers cannot deal with constraints. For this reason, the main purpose of this work is to provide a framework to enhance the control layer in the LOPA, which consists of a model predictive control (MPC) with safety features. These features include: sublayers in the controller system (such as real time optimization, target calculation, and MPC), safety constraints, and guarantee of stability by adopting an Infinite Horizon MPC (IHMPC). Here, we propose an approach for control-inspired view to process safety, replacing the BPCS by an Advanced Process Control System (APCS). Moving forward with these concepts, first, a literature review emphasizes the content, showing two perspectives for the APCS. The APCS is designed for two varieties of controllers, a basic IHMPC and IHMPC with zone control to compare the performance. In this framework, the first sublayer consists of a real time optimization (RTO) structure, that calculates the optimal operating condition for the process controller, which computes the control action. Besides, RTO has an additional constraint called the safety index, based on the protection of process operational. RTO and basic IHMPC communicate directly, while for IHMPC with zone control there is an inner sublayer called Target Calculation, it computes a feasible target to the controller, working as another safety strategy in APCS. After that, we demonstrate both structures applied to a CSTR reactor. From the case study, we compared both controllers, and evaluated the effect that the safety index constraint causes in the setpoints, outputs, and control actions. The use of safety constraint in RTO proved to be a safe strategy for the control layer, as well as IHMPC with zone control presented a safer profile than basic IHMPC. Furthermore, the results show that safety constraint affect the economic goal, decreasing its value.     

Dynamic response characteristics and structural optimization of isolation seal under the impact of gas explosion

Either in the chemical process plants or in the underground infrastructures, the isolation seal is regularly used to separate the working sections and inactive sections, or to isolate the possible explosion sites in order to avoid any domino effects. Due to differences in accumulation space or ignition point locations, pressure on the seal can vary when an explosion occurs. Thus, the safety and reliability of seals are crucial to maintaining safety in process industry. This paper focuses on seals constructed with concrete and loess materials, and examines the dynamic response characteristics of the gas explosion wave on the seal through sample experiments and numerical simulation metods. The study proposes an optimized design for the explosion-proof structure of the wedge-shaped and spherical seal, which can provide a technical basis for the explosion-proof and anti-explosion measures of various sealed walls. These research findings can also serve as a basis for improving the construction quality of seals.     

Optimizing the operation of the Qingshitan Reservoir in the Lijiang River for multiple human interests and quasi-natural flow maintenance

For reservoir operation, maintaining a quasi-natural flow regime can benefit river ecosystems, but may sacrifice human interests. This study took the Qingshitan Reservoir in the Lijiang River as a case, and developed an optimization model to explore a trade-off solution between social-economic interests and nature flow maintenance on a monthly base. The objective function considered irrigation, cruise navigation and water supply aspects. An index of flow alteration degree was proposed to measure the difference between the regulated discharge and the natural flow. The index was then used as an additional constraint in the model besides the conventional constraints on reservoir safety. During model solving, different criteria were applied to the index, representing various degrees of alteration of the natural flow regime in the river. Through the model, a relationship between social-economic interests and flow alteration degree was established. Finally, a trade-off solution of the reservoir operation was defined that led to a favorable social-economic benefit at an acceptable alteration of the natural flow.     

基于环境风险后果评价的危险化学品码头选址

在危险化学品码头选址期间开展环境风险后果评价,有助于减轻码头运营期间突发水污染事故对周围环境可能造成的不利影响。通过将环境风险后果具体化为风险受体脆弱性与暴露的乘积,把风险的受体分为人群、经济和生态3大类别,对每一类受体分别选取指标计算脆弱性指数。并以长江南京段沿岸危化品码头的选址为例,在评价区域建立二维水质模型模拟苯胺泄漏后污染物的时空分布,并根据不同的浓度阈值确定每类受体对应的暴露系数,最后给出定量的评价结果为选址提供建议。     

Use of agricultural land evaluation and site assessment in Whitman County,Washington, USA

Whitman County, Washington, USA, one of the largest wheat producing counties in the country, developed a land evaluation and site assessment system for evaluating proposed conversions of agricultural land to heavy commercial uses. The system uses soil potential indices to determine a land evaluation score and a nine-factor site assessment system to weigh suitability for development. Details on each of these are provided along with results for four sample sites.Scientific Paper no. 7165, College of Argiculture and Home Economics Research Paper, Washington State University, Pullman, Washington, project no. 0010.     

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

● Data acquisition and pre-processing for wastewater treatment were summarized. ● A PSO-SVR model for predicting COD_eff in wastewater was proposed. ● The COD_eff prediction performances of the three models in the paper were compared. ● The COD_eff prediction effects of different models in other studies were discussed. The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination (R²) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R², and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.     

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality. ● Improved model quickly converges to the global optimal fitness and remains stable. ● The prediction accuracy of water quality parameters is significantly improved. Water quality prediction is vital for solving water pollution and protecting the water environment. In terms of the characteristics of nonlinearity, instability, and randomness of water quality parameters, a short-term water quality prediction model was proposed based on variational mode decomposition (VMD) and improved grasshopper optimization algorithm (IGOA), so as to optimize long short-term memory neural network (LSTM). First, VMD was adopted to decompose the water quality data into a series of relatively stable components, with the aim to reduce the instability of the original data and increase the predictability, then each component was input into the IGOA-LSTM model for prediction. Finally, each component was added to obtain the predicted values. In this study, the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and prediction. The experimental results showed that the prediction accuracy of the VMD-IGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition (EEMD), the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Autoregressive Network with Exogenous Inputs (NARX), Recurrent Neural Network (RNN), as well as other models, showing better performance in short-term prediction. The current study will provide a reliable solution for water quality prediction studies in other areas.     

Modeling using dynamic variables – An approach for the design of loss prevention programs

Records from The National Safety Council [National Safety Council, 2007. Safety Intervention Evaluation: A Systematic Approach. <http://www.acgih.org/events/ControlBand/Thomas_SafetyIntervention.pdf> (accessed 10.01.07)] have shown that in 2004 alone, on-the-job injuries to workers constituted 35% of total recorded injuries in the United States. This generated an associated cost of about $142.2 billion. Unfortunately, the safety intervention programs enforced at work places to mitigate such losses are driven mainly by intuition and experience of involved safety personnel. This paper details implementing a computer program to furnish safety personnel with an empirical basis for designing loss prevention programs based on historical safety data. The computer tool is driven by a dynamic mathematical model which adapts itself to variations in data patterns and explains the correlation between historical incident rates and corresponding resources committed to interventions. This study empowers the industry with a tool that is capable of forming the core of optimizing valuable human resource allocation in safety program designs.     

Minimization of socioeconomic disruption for displaced populations following disasters

In the aftermath of catastrophic natural disasters such as hurricanes, tsunamis and earthquakes, emergency management agencies come under intense pressure to provide temporary housing to address the large‐scale displacement of the vulnerable population. Temporary housing is essential to enable displaced families to reestablish their normal daily activities until permanent housing solutions can be provided. Temporary housing decisions, however, have often been criticized for their failure to fulfil the socioeconomic needs of the displaced families within acceptable budgets. This paper presents the development of (1) socioeconomic disruption metrics that are capable of quantifying the socioeconomic impacts of temporary housing decisions on displaced populations; and (2) a robust multi‐objective optimization model for temporary housing that is capable of simultaneously minimizing socioeconomic disruptions and public expenditures in an effective and efficient manner. A large‐scale application example is optimized to illustrate the use of the model and demonstrate its capabilities ingenerating optimal plans for realistic temporary housing problems.     

基于微粒群算法的供水管网抗震优化设计

以管网年费用折算值为优化目标、管网拓扑结构与管径为优化参数、管网节点最低可靠度为约束条件,建立了供水管网抗震优化设计模型。利用微粒群算法对这一模型进行了求解,该算法以管网作为微粒个体,通过不断地更新微粒的位置来搜索最优的管网结构,直到最后给出优化的管网结构。利用上述方法对一典型供水管网进行了抗震优化设计分析,给出了3种不同节点最低可靠度约束条件下的优化改造方案。