新型城市污水脱氮除磷工艺的试验研究与优化设计

提出了一种改进型A2/O脱氮除磷工艺(MAAO),通过工艺试验和活性污泥2D模型对其进行运行和设计优化.结果表明,系统对COD、TN、NH 4-N和TP的平均去除率分别为85.7%、66.8%、97.35%和78.1%;当COD在300mg/L以上时,脱氮除磷效果均较好,可分别达到70%和90%以上.基于试验结果建立的MMAO工艺数学模型仿真效果良好,可对各单元组合进行优化,校核预测不利工况下的工艺运行效果,实现工艺不同工况下的运行参数优化,较好地实现新工艺的机理性优化设计和运行. MMAO稳定运行出水可达到<城镇污水处理厂污染物排放标准>(GB 18918-2002)一级B标准.由于工艺省去内回流,增加厌氧混合液部分超越提供反硝化碳源,使得运行费用明显降低;系统总停留时间比目前传统的以去除有机污染物为主的生物处理工艺略低,该工艺非常适用于对不具有脱氮除磷功能污水厂的改造.     

基于物料衡算法的复杂化工过程产排污量核算软件的开发及应用

物料衡算法是污染源普查技术规定的污染物排放量核算的一种重要方法,但对于复杂的化工过程,物料衡算法的难度和计算工作量均很大,难以为基层环境监察人员掌握和运用,同时运用物料衡算进行污染物排放量核算的可靠性,也很难保证。结合排污量核定实践,综合进行物料衡算的技巧和一般方法,科学地模化物质转化转移过程,以此为基础,应用Visual Basic高级计算机语言编制了针对复杂化工过程的物料衡算计算软件,并以沈阳某制药有限公司的脑复康生产过程为原型,利用实测资料对计算模型进行了验证,应用该软件计算某制药厂VC生产线等4条生产线的产排污量核算。该软件应用简便,可以方便快捷地进行复杂化工过程的物料衡算,并可以分析生产过程中的主要产污环节,为提高清洁生产水平提供科学依据。     

Performance evaluation of process industries resilience: Risk-based with a network approach

Risk management can be defined as coordinated activities to conduct and control an organization with consideration of risk. Recently, risk management strategies have been developed to change the approach to hazards and risks. Resilience as a safety management theory considers the technical and social aspects of systems simultaneously. Resilience in process industries, as a socio-technical system, has four aspects of early detection, error-tolerant design, flexibility, and recoverability. Meanwhile, process industries' resilience has three phases: avoidance, survival, and recovery, determining the transition between normal state, process upset event, and catastrophic event. There may be various technical and social failures such as regulatory and human or organizational items that can lead to upset or catastrophic events. In the avoidance phase, the upset event is predicted, and thus, the system remains in a normal state. For the survival phase, the system state is assumed to be an upset process event, and the system tries to survive through the unhealthy process conditions or remains in the same state, probably with low performance. In the recovery phase, the system is supposed to be catastrophic, and the emergency barriers are prioritized to show the severity of the consequences and response time, leading to a resumption of a normal state. Therefore, a resilience-based network can be designed for process industries to show its inherent dynamic transition in nature. In this study, network data envelopment analysis (DEA), as a mathematical model, is used to evaluate the relative efficiency of the process industries regarding a network transition approach based on the system's internal structure. First, a resilience-based network is designed to consist of three states of normal, upset, and catastrophic events. Then, the efficiency of each industrial department, which is defined as decision-making units (DMUs), is evaluated using network DEA. As a case study, a refinery that is considered a critical process industry is assessed. Using the proposed model shows the efficient and inefficient DMUs in each of three states of normal, upset, and catastrophic events of the process and the projection onto efficient frontiers. Besides calculating the network efficiency, the performance of each state is extracted to precisely differentiate between DMUs. The results of this study, which is one of the fewest cases in the area of performance evaluation of process industries with a network approach, indicated a robust viewpoint for monitoring and assessment of risks.     

SBBR工艺外加碳源乙酸钠脱氮效果的实验研究

试验选取乙酸钠为外加碳源,采用SBBR工艺处理低C/N比城市生活污水。当外加碳源后的C/N比值增加至7.0左右时,对原水NH_4~+-N去除率最高为89.31%,外加碳源后的C/N为7.12时,TN的去除率最好,去除率为71.27%。结果表明,外加乙酸钠碳源后的SBBR工艺对于低C/N生活污水脱氮性能良好。出水水质指标均达到了《城镇污水处理厂污染物排放标准》(GB18918—2002)中的"一级标准A标准",出水可作为回用水。     

Villains,victims, and heroes: Accounting for the roles human activity plays in LOPA scenarios

When a team is analyzing a LOPA scenario, the team needs to consider all three roles played by human interaction in the scenario: that of cause, as a result of human error; that of receptor, both in terms of safety impacts (inside the fence line) and community impacts (outside the fence line); and that of independent layer of protection (IPL), considering both administrative controls and human responses. Frequently, the nature of these three roles are inter-related, and setting guidance that is internally consistent is important to using LOPA to assess risk rather than as a means to game the analyses to simply achieve a wished-for result.A number of criteria have been proposed to quantify human involvement, typically as cause, as receptor, or as IPL. Establishing a framework to look at all three in a unified way is more likely to result in analyses that are consistent from scenario to scenario.This paper describes such a framework and presents it in a way that allows organizations to review their own criteria for quantifying human involvement in LOPA. It also examines some of the published LOPA criteria for human involvement and looks at them in terms of consistency of approach between evaluation of cause, receptor, and IPL. Finally the paper makes suggestions to use in calibrating LOPA methodologies to achieve consistent and believable results in terms of human interaction within and between scenarios that have worked for other organizations.     

Requirements for improved process hazard analysis (PHA) methods

In order to develop better process hazard analysis (PHA) approaches, weaknesses in current approaches first must be identified and understood. Criteria can then be developed that new and improved approaches must meet. Current PHA methods share common weaknesses such as their inability specifically to address multiple failures, their identification of worst-consequence rather than worst-risk scenarios, and their focus on individual parts of a process. There has been no comprehensive analysis of these systemic weaknesses in the literature. Weaknesses are identified and described in this paper to assist in the development of improved approaches. Knowledge of the weaknesses also allows PHA teams to compensate for them to the extent possible when performing studies.Key criteria to guide the development of improved methods are proposed and discussed. These criteria include a structure that facilitates meaningful brainstorming of scenarios, ease of understanding and application of the method by participants, ability to identify scenarios efficiently, completeness of scenario identification, exclusion of extraneous scenarios, ease of updating and revalidating studies, and ease of meeting regulatory requirements. Some proposals are made for moving forward with the development of improved methods including the semi-automation of studies and improvements in the training of team members.     

Targeting for optimal grid-wide deployment of carbon capture and storage (CCS) technology

Carbon capture and storage (CCS) techniques are considered as one of the promising approaches to reduce carbon dioxide (CO₂) emissions from fossil fuel based power generation, which still accounts for a significant portion of greenhouse gas emissions in the world. CCS technology can be used to mitigate greenhouse gas emissions, with the additional advantage that it allows continuing use reliable and inexpensive fossil fuels. However, CCS retrofit entails major capital costs as well as a reduction of overall thermal efficiency and power output. Thus, it is essential for planning purposes to implement the minimal extent of CCS retrofit while meeting the specified carbon emission limits for the power sector. At the same time, it is necessary to plan for compensatory power generation capacity to offset energy losses resulting from CCS retrofit. In this paper, an algebraic targeting technique is presented for planning of grid-wide CCS retrofits in the power generation sector with compensatory power. The targeting technique is developed based on pinch analysis. In addition, the proposed methodologies are illustrated through case studies based on grid data in India and the Philippines. Sensitivity analysis is carried out to determine the suitable CCS technology and compensatory power source which satisfy emission limits.     

Optimizing the design of storage facilities through the application of ISD and QRA

Four strategies can be used to achieve safety in chemical processes: inherent, passive, active and procedural. However, the strategy that offers the best results is the inherent safety approach, especially if it is applied during the initial stages of a project. Inherently Safer Design (ISD) permanently eliminates or reduces hazards, and thus avoids or diminishes the consequences of incidents. ISD can be applied using four strategies: substitution, minimization, moderation and simplification. In this paper, we propose a methodology that combines ISD strategies with Quantitative Risk Assessment (QRA) to optimize the design of storage installations. As 17% of major accidents in the chemical industry occur during the storage process and cause significant losses, it is essential to improve safety in such installations. The proposed method applies QRA to estimate the risk associated with a specific design. The design can then be compared to others to determine which is inherently safer. The risk analysis may incorporate complex phenomena such as the domino effect and possible impacts on vulnerable material and human elements. The methodology was applied to the San Juanico tragedy that occurred in Mexico in 1984.     

Major accident management in the process industry: An expert tool called CESMA for intelligent allocation of prevention investments

A tool (called CESMA) was developed to carry out cost–benefit analyses and cost-effectiveness analyses of prevention investments for avoiding major accidents. A wide variety of parameters necessary to calculate both the costs of the considered preventive measures and the benefits related with the avoidance of accidents were identified in the research. The benefits are determined by estimating the difference in (hypothetical) major accident costs without and with the implementation of a preventive measure. As many relevant costs and benefits as possible were included into the tool, based on literature and expert opinion, in order to be able to deliver an all-embracing cost–benefit analysis and cost-effectiveness analysis to assist in the investment decision process. Because major accidents are related to extremely low frequencies, the tool takes the uncertainty of the unwanted occurrence of a major accident into account through the usage of a so-called ‘disproportion factor’. Compared with existing software, the CESMA tool is innovative by striving for an as-accurate-as-possible picture of costs and benefits of major accident prevention, and taking the uncertainties accompanying disastrous events into consideration. Furthermore, an illustrative example of CESMA is presented in the paper.     

Simulation-based fault propagation analysis—Application on hydrogen production plant

Recently production of hydrogen from water through the Cu–Cl thermochemical cycle is developed as a new technology. The main advantages of this technology over existing ones are higher efficiency, lower costs, lower environmental impact and reduced greenhouse gas emissions. Considering these advantages, the usage of this technology in new industries such as nuclear and oil is increasingly developed. Due to hazards involved in hydrogen production, design and implementation of hydrogen plants require provisions for safety, reliability and risk assessment. However, very little research is done from safety point of view. This paper introduces fault semantic network (FSN) as a novel method for fault diagnosis and fault propagation analysis by using evolutionary techniques like genetic programming (GP) and neural networks (NN), to uncover process variables’ interactions. The effectiveness, feasibility and robustness of the proposed method are demonstrated on simulated data obtained from the simulation of hydrogen production process in Aspen HYSYS^®. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.