Water-using Units Research Articles

Two-Step Optimization Approach for Design of A Total Water System

The development of the two-step optimization approach, as seen in Part 1 of the paper, can then be extended to a total water system. The total water system comprises two subsystems: namely, water-using units and wastewater treatment system. In this paper, the proposed approach utilizes mixed integer linear programming (MILP) and nonlinear programming (NLP) in the two-step optimization approach to generate multiple optimum solutions, called “class of good solutions”, for the total water system design problem. A case study from the literature is used to illustrate the proposed approach, and comparisons with the results from the current techniques were then made to demonstrate the proposed method’s strength.

Wastewater minimization under uncertain operational conditions

This paper addresses the problem of uncertainty in optimizing water networks in process industries. Due to the fact that wastewater flow rates as well as the levels of contaminants may vary widely as a result of changes in operational conditions and/or feedstock and product specifications, optimal wastewater network designs should be resilient and able to accommodate such changes. Uncertainties considered in this study are derived from actual operational practice of major water-using units in a typical oil refinery of 400,000 barrels/day throughput. Rather than directly varying the concentrations and mass loads, only seasonal effects have been considered in this research to illustrate applications of the models. Sensitivity analyses reveal that introducing uncertainty in operating conditions results in considerable changes in the connectivity of the units involved in wastewater reuse. The proposed stochastic optimization model produces a flexible wastewater network which is capable of accommodating uncertainties in operating temperature. In the presence of uncertainties, the optimal network minimizes the impact on the reuse connectivity (topology) by providing 32.2 t/h of freshwater in addition to the condensing steam. The stochastic approach adopted in this research has been found to be effective in handling uncertainties and has resulted in flexible and resilient wastewater networks with low expected value of perfect information (EVPI).

Water Usage and Treatment Network Design Using Genetic Algorithms

A mathematical programming model has been developed in this work to identify the cost-optimal and least-consumption water usage and treatment networks (WUTNs). The network configurations of WUTNs were generated on the basis of a superstructure embedded with the existing water-using units, the repeated water-treatment units, and the mixers. The benefits of using this model are clearly demonstrated in the examples. The genetic algorithms were used in this study to solve the WUTN optimization problem. Several techniques have been developed to enhance convergence in the evolution processes. First of all, by adopting the split fractions from splitting nodes as the design variables and devising an encoding strategy accordingly, the search space can be significantly reduced. Second, by cascading a series of evolution processes according to a set of “inducing” parameters, the appropriate ranges of the design variables can be efficiently determined. As a result, the need to obtain a good initial guess can be eliminated. The total number of generations to reach optimum can also be lowered to an acceptable level even for a large WUTN design problem.

Optimal water allocation in a petroleum refinery

A method for solving the planning problem of optimal water allocation is presented. All the alternative systems are combined into an integrated system by employing structure variables or split ratios at the point where a water stream is split into more than two streams. The values of structure variables are determined for given process conditions so as to minimize the total cost, subject to constraints derived from material balances and interrelationships among water-using units and wastewater-treating units. The optimization problem is solved by using the Complex method. The method is illustrated by its application to the water allocation problem in a petroleum refinery.

Water Use and Yields of Sugarbeets Over a Range from Excessive to Limited Irrigation

AbstractWater use and sucrose yield of sugarbeets (Beta vulgaris L. cv. USH 10) as a function of decreasing water applications were investigated to determine if sucrose yield per unit of water use would increase with limited irrigation. The sugarbeets were fall‐planted in a silty clay soil and irrigated uniformly for 6 weeks to establish seedling stands and to wet soil to the 152‐cm depth. Thereafter, 102 mm water was applied whenever cumulative ET from sugarbeets growing in an adequately watered weighing lysimeter reached 91, 102, 112, 122, 133, or 143 mm water. Soil was sampled to the 152‐cm depth, in 30.5‐cm increments, before each irrigation after 12 November and at harvest. Water use was calculated from changes in soil water content between irrigations and applied water.Water applications, after the initial uniform applications, varied from 609 to 940 mm in six to nine irrigations plus 25 mm rainfall. Water use was linearly related to lysimeter ET. During differential treatment, water use varied from 11% above to 23% below the amount needed to replace lysimeter ET. Total seasonal water use varied from 900 to 1,195 mm. Lysimeter ET averaged 67% of pan evaporation after leaves completely covered the soil. Neither sucrose content nor yield was affected by irrigation treatments. Sucrose yields averaged 13 and 18 metric tons/ha on 14 May and 24 June, respectively. With limited irrigation, sucrose yield per unit of water use increased as much as 22% above the treatment that matched lysimeter ET. The soil salinity did not increase sufficiently to require immediate leaching for any of the treatments.