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Water allocation problem

we consider an application related to the paper making process and study a water allocation problem for an integrated plant containing a thermomechanical pulping plant and a paper mill. For details of this problem with three objectives, see Hakanen et al. (2004). [Pg.176]

The upper part of Fig. 6.4 represents the thermomechanical pulping plant and the lower part represents the paper mill. The goal is to minimize the amount of fresh water taken into the process and also to minimize [Pg.176]


Water allocation problems have nonlinearities and non-convexities due to bilinear terms. To address this issue we propose to discretize one of the variables of the bilinear terms. As a result an MILP model is generated, which provides a lower bound. To reduce the gap between this lower bound and the upper bound (a feasible solution found using the original NLP model), an interval elimination procedure is proposed. As a result, the feasible space shrinks after each iteration and the global optimum is identified. We illustrate the methodology for minimum water allocation problems. [Pg.43]

Problem statement Given a set of water using units, freshwater sources, wastewater sink and available regeneration processes with their limiting data, a globally optimum for the freshwater consumption is sought. The corresponding non-liner model to solve this water allocation problem (WAP) written in terms of contaminant mass load is ... [Pg.43]

Abstract. In the present paper the problem of reuse water networks (RWN) have been modeled and optimized by the application of a modified Particle Swarm Optimization (PSO) algorithm. A proposed modified PSO method lead with both discrete and continuous variables in Mixed Integer Non-Linear Programming (MINLP) formulation that represent the water allocation problems. Pinch Analysis concepts are used jointly with the improved PSO method. Two literature problems considering mono and multicomponent problems were solved with the developed systematic and results has shown excellent performance in the optimality of reuse water network synthesis based on the criterion of minimization of annual total cost. [Pg.282]

Poplewski, G. and Jezowski, J.M. (2010) Application of adaptive random search optimization for solving industrial water allocation problem, in Stochastic Global Optimization Techniques and Applications in Chemical Engineering (ed. G.P. Rangaiah), World Scientific, Singapore. [Pg.373]

But each spin is shared by one other tetrahedron, so the 16 configurations per tetrahedron are not independent. Following Pauling s arguments for the water ice problem, 2 = 4 spins are allocated for each tetrahedron and only 6/16 of these satisfy the zero total moment condition. Thus, the true degeneracy, Qq = [2 (6/16)]. Then, the total entropy is 5 = =... [Pg.52]

Figure 7.13 is structural representation of segregation, mixing, and direct recycle candidate strategies for the problem. Each source is split into several frac-tions that can be fed to a sink. The flowrate of the streams passed from source w to sink u is referred to as The terms F, Z", and represent the inlet flowrate, inlet composition, and outlet flowrate of the streams associated with unit u. Since mixing is embedded, there is no need to include the mixing tank (m = 4) or the source that it generates u> = 5) in the analysis. Unless recycle of biotreatment effluent is considered, there is no need to represent the biotreatment sink in Fig. 7.13. However, streams allocated to biotreatment should be represented and their flowrates are referred to as (m = 5 is the biotreatment sink). Finally, fresh water may be used in any unit at a flowrate of Fresh,. Figure 7.13 is structural representation of segregation, mixing, and direct recycle candidate strategies for the problem. Each source is split into several frac-tions that can be fed to a sink. The flowrate of the streams passed from source w to sink u is referred to as The terms F, Z", and represent the inlet flowrate, inlet composition, and outlet flowrate of the streams associated with unit u. Since mixing is embedded, there is no need to include the mixing tank (m = 4) or the source that it generates u> = 5) in the analysis. Unless recycle of biotreatment effluent is considered, there is no need to represent the biotreatment sink in Fig. 7.13. However, streams allocated to biotreatment should be represented and their flowrates are referred to as (m = 5 is the biotreatment sink). Finally, fresh water may be used in any unit at a flowrate of Fresh,.
Negative externalities arise when an action by an individual or a group implies harmful effects on others such as unintended dispersion of chemicals to land, air and water air pollution effects on health forest growth or fish reproduction. When negative externalities are generated they should be internalized into the market economy. By internalizing the externalities the economic value of environmental impacts are allocated to the pollution sources and included in the economics of the activities causing the problem. This would also allow for the market to function properly and thereby reach a socially optimal level of environmental impacts. [Pg.115]

Plant operations deal with the allocation of raw materials on a daily or weekly basis. One classical optimization problem, which is discussed later in this text, is the allocation of raw materials in a refinery. Typical day-to-day optimization in a plant minimizes steam consumption or cooling water consumption. [Pg.7]

Another problem of significance is the optimum policy of water recycling. This subject is in itself substantial and cannot be handled here. An economical approach involves optimal allocation of streams, both as flow rates and contaminant concentration. The analysis may be performed systematically with tools based on the concept of water pinch and mass-exchange networks . This subject is treated thoroughly in specialized works, as in the books of El-Halwagi [19] and Smith [20]. A source-sink mapping technique developed around the acrylonitrile plant may be found in the book of Allen and Shoppard [21]. [Pg.332]

Given the set of x and y values for the nodes in the network, the allowable connections and the pipe diameter, d, (chosen from a discrete set) allocated to each possible connection, the evaluation of the objective function is based on identifying all the matches defined by the positions of the lines in the discrete space. This evaluation is deterministic and enables the identification of the network layout and the direct evaluation of the cost of the water distribution network. This objective function forms the basis of a discrete optimization problem in x, y, and d. [Pg.120]

The problem seems to be acute if natural resources such as land and water are switched from food production to biofuel (Negash and Swinnen, 2013). In Ethiopia, about one-third of poor farmers have allocated 15% of their land to castor bean cultivation to produce biofuel (Negash and Swinnen, 2013) however, this is not an issue in India, since the government limited fuel crops to marginal lands and wastelands (Ravindranath et al., 2011). In the end, it s... [Pg.27]


See other pages where Water allocation problem is mentioned: [Pg.48]    [Pg.21]    [Pg.176]    [Pg.178]    [Pg.348]    [Pg.48]    [Pg.21]    [Pg.176]    [Pg.178]    [Pg.348]    [Pg.172]    [Pg.1015]    [Pg.11]    [Pg.87]    [Pg.94]    [Pg.93]    [Pg.554]    [Pg.162]    [Pg.57]    [Pg.1083]    [Pg.158]    [Pg.67]    [Pg.187]    [Pg.447]    [Pg.68]    [Pg.340]    [Pg.282]    [Pg.293]    [Pg.117]    [Pg.446]    [Pg.56]    [Pg.16]    [Pg.45]    [Pg.538]    [Pg.99]   
See also in sourсe #XX -- [ Pg.21 , Pg.176 ]




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