Process integration design methods for water conservation and wastewater reduction in industry

This three part series of papers addresses operational techniques for applying mass integration principles to design in industry with special focus on water conservation and wastewater reduction. Part 1 covers design techniques for any number of wastewater streams containing a single contaminant. The technique comprises a two stage graphical approach. In the first stage, the water pinch diagram is used to identify key design targets (such as the minimum freshwater requirement of the studied system, the amount of achievable water recycling and reuse and the water quality concentration bottleneck) for the industrial process of interest. Key practical insights provided by the water pinch diagram are discussed. In the second stage, source–sink mapping diagrams are used to identify the water recycling and reuse network, and any alternative networks, that achieve the identified targets. A case study is included to illustrate the proposed methodology. Electronic Publication     

A study on establishing an optimal water network in a dyeing and finishing industrial park

The problem caused by large water consumption in a dyeing and finishing industrial park needs to be solved urgently. In this paper, a mathematical programming model for water integration in the dyeing and finishing industrial park (DFIP) is established. This model can optimize the water network on the basis of the minimum total annual cost. The superstructure, base of the formulas, considers shared multistage wastewater treatment facilities according to the real situation of DFIP, which is not involved in the previous study. It allows the wastewater reuse in the same plant and the water exchange with different plants. The treated water from each stage of shared multistage treatment facilities could be segregated and directed to the plants for reuse, to the next stage for advanced treatment, and/or discharged to the environment. In addition, this study also develops a water metabolism analytical method to ensure the practicability of the model. It mainly functions as analyzing the wastewater quality and water quality requirement for dyeing and finishing plant, which aims at both exploring potential water recovery and amending impractical water connections. The results obtained in a practical example indicate that the water reuse rate of the optimized water network reaches up to 73 % and the total annual cost can be decreased by 54 %.     

Exact topology discovery algorithm for the capacity and delay constrained loop network

We have developed an exact model and algorithm for the delay-constrained minimum cost loop problem (DC-MCLP) of finding broadcast loops from a source node. While the traditional minimum cost loop problem (MCLP) deals with only the traffic capacity constraint served by a port of source node, the DC-MCLP deals with the mean network delay and traffic capacity constraints simultaneously. The DC-MCLP consists of finding a set of minimum cost loops to link end-user nodes to a source node satisfying the traffic requirements at end-nodes and the required mean delay of the network. In the DC-MCLP, the objective function is to minimise the total link cost. We have formulated the DC-MCLP and proposed an exact algorithm for its solution. The proposed algorithm is composed of two phases: in the first phase, it generates feasible paths to satisfy the traffic capacity constraint; in the second phase it finds the exact loop topology through matching and allocating optimal link capacity to satisfy the mean delay constraint. In addition, we have derived several properties including the memory and time complexity of the proposed algorithm. Performance evaluation shows that the proposed algorithm has good efficiency for networks with less than thirty nodes and light traffic. Our proposed algorithm can be applied to find the broadcast loops for real-time multimedia traffic     

Exact algorithm for delay-constrained capacitated minimum spanning tree network

The delay-constrained capacitated minimum spanning tree (DC-CMST) problem of finding several broadcast trees from a source node is discussed. While the traditional CMST problem deals with only the traffic capacity constraint served by a port of the source node, and delay-constrained minimum spanning tree (DCMST) considers only the maximum end-end delay constraint, the DC-CMST problem deals with both the mean network delay and traffic capacity constraints. The DC-CMST problem consists of finding a set of minimum cost spanning trees to link end-nodes to a source node satisfying the traffic requirements at end-nodes and the required mean delay of the network. In the DC-CMST problem, the objective function is to minimise the total link cost. A dynamic programming-based three-phase algorithm that solves the DC-CMST problem is proposed. In the first phase, the algorithm generates feasible solutions to satisfy the traffic capacity constraint. It finds the CMSTs in the second phase, and allocates the optimal link capacities to satisfy the mean delay constraint in the third phase. Performance evaluation shows that the proposed algorithm has good efficiency for any network with less than 30 nodes and light traffic. The proposed algorithm can be applied to any network regardless of its configuration, and used for the topological design of local networks and for efficient routing algorithms capable of constructing least cost broadcast trees.     

MILP model for emergy optimization in EIP water networks

The eco-industrial park (EIP) concept provides a framework in which several plants can cooperate with each other and exchange their wastewater to minimize total freshwater consumption. Emergy analysis is a methodology that considers the total, cumulative energy which has been consumed within a system; thus, by minimizing emergy, an environmentally optimal EIP can be designed. This article presents a mixed-integer linear programming (MILP) model for minimizing emergy of an interplant water network in an EIP. The methodology accounts for the environmental impacts of water use, energy consumption, and capital goods within the EIP in a balanced manner. The proposed technique is then demonstrated by solving a case study from literature.     

Chemical and physicochemical profile of wastewaters produced from the different stages of Spanish-style green olives processing

The main purpose of the processing of table olives is the removal, at least partially, of the natural bitterness of the fruit in order to render it edible. The preparation of Spanish-style green olives after harvesting involves cleaning followed by debittering using NaOH solution, washing with water, a lactic acid fermentation step and finally canning. Wastewaters originating from table olives processing industries pose an important environmental threat, as they are characterized by a very high organic load and high concentration of phenolic compounds, which are toxic to living organisms. In this communication, the chemical and physicochemical profile of wastewaters produced from the different stages of Spanish-Style green olives processing was investigated. Phenolic compounds, organic acids, amino acids and total sugars along with common physicochemical parameters were determined in order to appraise the specific features of each individually produced wastewater.     

Optimization of water network integrated with process models

In this paper, a novel approach for the synthesis of water network incorporated with process models is introduced. The process models are utilized to relate the variables (i.e., flow rate and concentration) of process output (typically defined as internal water source) with those of process input (i.e., water sink). A generalized water network superstructure is developed to embed all possible process units and all the connections among resources, interceptors, process units, and wastes. The problem is formulated as four optimization problems (minimum freshwater flow rate, intercepted flow rate, intercepted mass load, and number of connections), and the four models are solved in sequence to locate the targets. A literature case is used to validate the proposed approach. Moreover, a sour water network of a practical refinery plant is presented to illustrate the applicability and effectiveness of the proposed approach.     

Load management as a smart grid concept for sizing and designing of hybrid renewable energy systems

Optimal sizing of hybrid renewable energy systems (HRES) to satisfy load requirements with the highest reliability and lowest cost is a crucial step in building HRESs to supply electricity to remote areas. Applying smart grid concepts such as load management can reduce the size of HRES components and reduce the cost of generated energy considerably. In this article, sizing of HRES is carried out by dividing the load into high- and low-priority parts. The proposed system is formed by a photovoltaic array, wind turbines, batteries, fuel cells and a diesel generator as a back-up energy source. A smart particle swarm optimization (PSO) algorithm using MATLAB is introduced to determine the optimal size of the HRES. The simulation was carried out with and without division of the load to compare these concepts. HOMER software was also used to simulate the proposed system without dividing the loads to verify the results obtained from the proposed PSO algorithm. The results show that the percentage of division of the load is inversely proportional to the cost of the generated energy.     

Recycling of bleach plant effluent of an Indian paper mill using water cascade analysis technique

A systematic approach to optimizing water network has traditionally been utilized to examine and plan water conservation in industrial processes. In the present case study, water cascade analysis was used to analyze and optimize the water network of a bleaching section of an Indian paper mill. Water system integration can minimize both the freshwater consumption and wastewater discharge of a paper mill. Three limiting constraints, i.e., Chemical oxygen demand, Total dissolved solids, and Adsorbable organic halides (AOX), were considered for the study; after analysis, AOX was found to be a critical limiting constraint. A nearest neighbor algorithm (NNA) was used to distribute the fresh water and recycled water among the plant operations. After the application of WCA and NNA in the bleaching section, 41.75% of fresh water consumption and 70.67% of wastewater generation could be reduced.     

Statistical evaluation of lightning-related failures for the optimal location of surge arresters on the power networks

Direct lightning strokes cause unscheduled supply interruptions in power systems because of a failure of the insulation. Metal oxide surge arresters, as a proper protective device, have been widely adopted in power systems to reduce lightning initiated flashovers and, hence, increase the power quality and reliability of the systems. Based on a genetic algorithm approach, a cost effective solution is described to find the optimum location of surge arresters on a power network in order to minimise the global risk of the network, and to improve its reliability. A statistical approach to evaluate lightning failures has been introduced and an optimisation procedure developed to analyse the network in order to satisfy the power utility requirement for a specific value of risk and/or line performance with a minimum set of arresters, that is, at minimum cost. Not only the insulation flashover but also the failure of the arrester can affect the reliability of power systems. Therefore, both the failure of the insulation and that of the arrester are considered in the proposed method.     

顶底角钢连接半刚性钢结构抗震性能数值分析

为了研究顶底角钢连接半刚性钢结构的抗震性能,介绍相关文献中角钢的屈服荷载及初始刚度的计算,基于角钢的拉压试验结果,验证角钢的屈服荷载及初始刚度计算方法的准确性并对角钢的屈服荷载计算公式进行修正,进而提出角钢在反复拉压荷载下的恢复力模型;编制角钢连接单元,提出基于有限元软件OpenSEES的顶底角钢连接半刚性钢结构的建模方法,对顶底角钢连接半刚性钢结构低周往复试验、振动台试验进行数值模拟,所得结果与试验结果进行比较,结果表明:该文提出的建模方法具有单元少、自由度少、建模方便、计算效率高、耗时少等优点,采用该文方法进行的数值模拟与试验结果吻合较好,精度能满足工程需求。     

Generating realistic laminate fiber angle distributions for optimal variable stiffness laminates

The advent of advanced fiber placement technology has made it possible, through the use of fiber steering, to exploit the anisotropic properties of composite materials to a larger extent than was previously possible. Spatial variation of stiffness can be induced by steering composite fibers in curvilinear paths to give beneficial load and stiffness distribution patterns. Buckling of composite panels is one area where fiber steering has been proven to be very effective. Fiber angles and predefined fiber angle variations are used in most of the research on fiber steered composites reported in the literature, however, from an optimization point of view it is attractive to design such variable stiffness (VS) structures in terms of lamination parameters (LPs). This results in a two-step design approach. In the first step a VS composite is designed in terms of LPs, and in the second step the LPs are converted into fiber angle distributions for each layer in the laminate. A methodology is proposed to convert a known LP distribution for a VS composite laminate into a realistic design in terms of fiber angles, with minimum loss of structural performance, whilst satisfying a constraint on in-plane fiber angle curvature. The proposed conversion process is formulated as an optimization problem and can be used for any number of equi-thickness plies. The methodology was tested by converting a known optimal LP design for a sample structure, a square plate under bi-axial compression into a fiber angle design. The effect of the in-plane curvature constraint, the number of layers in the laminate, and the choice of objective function for the conversion process were studied for a balanced symmetric lay-up.     

On the use of graphical analysis for the design of batch water networks

This paper presents a series of graphical analyses for the design of batch water networks to process systems characterized by fixed flow rate operations. Water integration is carried out by exploiting all water reuse opportunities, with which both freshwater consumption and wastewater generation can be reduced at the same time. Throughout the analyzing procedure, the minimum freshwater consumption is first identified. Under the already set freshwater target, the follow-up analyses are continued to cut down the number of storage tanks for the simplification of the network complexity. The resultant network structure is finally obtained with the corresponding storage policy. After water integration to the literature case study, more than 54 and 63% of savings in freshwater, with more than 61 and 72% of reductions in wastewater are reported for the single and cyclic batch productions, respectively, compared to the base case without water reuse.     

A forward gradient time integration procedure for an internal variable constitutive model of Sn–Pb solder

A self-correcting forward gradient time integration procedure is formulated for the integration of a new unified visco-plastic constitutive model for an eutectic solder alloy using a tensorial internal state variable. The procedure has been implemented numerically into a commercial FE code through a user-material subroutine and is well suited to deal with numerically stiff constitutive equations. This is accomplished through an automatic subincrementation procedure controlled by a maximum allowed equivalent inelastic strain increment per time step. For illustration purposes, two examples are discussed: (i) uniaxial test simulations to reveal the optimum combination of the user-specified tolerance and the prescribed load step size to obtain a desired accuracy at a minimum cost, (ii) a large-scale three-dimensional analysis of an integrated circuit (IC) package's solder joint to show the capability of the proposed procedure to deal with thermomechanical loading.     

Optimum load time history for non-linear analysis using dynamic relaxation

A modification of the dynamic relaxation method is proposed which facilitates static analysis of non-linear problems. Continuous loading in time is adopted instead of the ordinary step function of time. Inertia and damping forces arising during the loading process are kept at a minimum using an optimum load time history. This results from the stationary condition of an appropriate functional. The equation of motion is included as a subsidiary condition. Continuous load—deflection curves can be obtained. An incremental solution is avoided. Application of the method is extremely simple. Existing programs based on explicit time integration schemes can be easily adapted for it. Sample solutions are presented.     

Application of groundwater cooling scheme for London Underground network

The demand for acceptable thermal comfort conditions within the London Underground network has grown over the years. The operation of the network generates a large heat load, with high temperatures recorded in summer in train and on platforms. Different cooling methods and new sources of cooling have been proposed for cooling the network. Of the new sources of cooling proposed, groundwater cooling is the first to be tried. London Underground Limited unique characteristics of being close to the aquifer of the central London basin, rivers and also because they pump over 30 million litres of water from the network every day make it an ideal site for this technology. This paper investigates thermal issues within London Underground network and presents an overview of groundwater cooling schemes for the network.     

Biological nitrate removal from wastewater of a metal-finishing industry

An upflow packed bed reactor at laboratory scale has been operated for a continuous period of 5 months to investigate the technical feasibility of biological nitrate removal applied to the effluent of the coagulation-sedimentation wastewater of a metal-finishing industry. The reactor was fed with industrial wastewater in a five-fold dilution to reproduce the global spill in the factory (20/80, industrial wastewater/domestic wastewater) with a concentration of nitrate between 141 and 210 gNO(3)-N/m(3). Methanol was added as a carbon source for denitrification. Inlet flow rate was progressively increased from 9 to 40 L/day (nitrogen input load from 45 to 250 gNO(3)-N/(m(3)h)). The highest observed denitrification rate was 135 gNO(3)-N/(m(3)h) at a nitrate load of 250 gNO(3)-N/(m(3)h), and removal efficiencies higher than 90% were obtained for loads up to 100 gNO(3)-N/(m(3)h). A mass relation between COD consumed and NO(3)-N removed around 3.31 was observed. Better results were achieved in a previous stage using tap water with nitrate added as a sole pollutant as a synthetic feed (critical load of 130 gNO(3)-N/(m(3)h) and denitrification rate of 200 gNO(3)-N/(m(3)h) at a nitrate load of 250 gNO(3)-N/(m(3)h)). This fact could indicate that the chemical composition of the industrial source hinders to some extent the performance of the biological process. Whatever case, results demonstrated the viability of the denitrification process for the global industrial wastewater. A simple model based on Monod kinetics for substrate consumption, and constant biomass concentration was applied to model the industrial wastewater treatment, and a reasonably good fitting was obtained.     

An Effective Classifier Model for Imbalanced Network Attack Data

Recently, machine learning algorithms have been used in the detection and classification of network attacks. The performance of the algorithms has been evaluated by using benchmark network intrusion datasets such as DARPA98, KDD’99, NSL-KDD, UNSW-NB15, and Caida DDoS. However, these datasets have two major challenges: imbalanced data and high-dimensional data. Obtaining high accuracy for all attack types in the dataset allows for high accuracy in imbalanced datasets. On the other hand, having a large number of features increases the runtime load on the algorithms. A novel model is proposed in this paper to overcome these two concerns. The number of features in the model, which has been tested at CICIDS2017, is initially optimized by using genetic algorithms. This optimum feature set has been used to classify network attacks with six well-known classifiers according to high f1-score and g-mean value in minimum time. Afterwards, a multi-layer perceptron based ensemble learning approach has been applied to improve the models’ overall performance. The experimental results show that the suggested model is acceptable for feature selection as well as classifying network attacks in an imbalanced dataset, with a high f1-score (0.91) and g-mean (0.99) value. Furthermore, it has outperformed base classifier models and voting procedures.