A Mixed Integer Linear Programming (MILP) Model for Optimal Design of Water Network

This work presents the development of a new systematic technique to target fresh water consumption and wastewater generation for systems involving multiple contaminants when all options of water minimization including source elimination, reduction, reuse/recycle, outsourcing and regeneration are considered simultaneously. This problem is formulated as mixed integer linear programming (MILP) and implemented in Generalized Algebraic Modeling System (GAMS). The consideration of process changes will lead to optimal design of minimum water utilization network. The MILP model proposed in this work can be used to simultaneously generate the minimum water targets and design the minimum water network for global water-using operations for buildings and industry. The approach is illustrated by using an industrial involving a chlor-alkali plant. Significant water savings for the industrial case study is achieved, illustrating the effectiveness of the proposed approach

An Artificial Intelligence Approach to Estimate Travel Time along Public Transportation Bus Lines

Public transportation sectors have played significant roles in accommodating passengers and commodities efficiently and effectively. The modes of public transportation often follow pre-defined operation schedules and routes. Therefore, planning these schedules and routes requires extensive efforts in analyzing the built environment and collecting demand data. Once a transit route is operational as an example, collecting and maintaining real-life information becomes an important task to evaluate service quality using different Key Performance Indicators (KPIs). One of these KPIs is transit travel time along the route. This paper aims to develop a transit travel time prediction model using an artificial intelligence approach. In this study, 12 public bus routes serving the Greater City of Doha were selected. While the ultimate goal is to predict transit travel time from the start to the end of the journeys collected over a period of one-year, routespecific inputs were used as inputs for this prediction. To develop a generalized model, the input variables for the transit route included the number and type of intersections, number of each type of turning movements and the built environment. An Artificial Neural Networks (ANN) model is used to process 78,004 valid datasets. The results indicate that the ANN model is capable of providing reliable and accurate transit travel time estimates, with a coefficient of determination (R2) of 0.95. Transportation planners and public transportation operators can use the developed model as a tool to estimate the transit travel time

Systematic Procedure for Generating Operational Policies to Achieve Target Crystal Size Distribution (CSD) in Batch Cooling Crystallization

Batch cooling crystallization is one of the important unit operations involving separation of solid-liquid phases. Usually the most common crystal product qualities are directly related to the crystal size distribution (CSD). However the main difficulty in batch crystallization is to obtain a uniform and reproducible CSD. Therefore supersaturation control can be applied to drive the process within the metastable zone and thereby enhance the control of the CSD. Although this approach has been shown to produce high quality crystals, the set point operating profiles for the supersaturation controller are usually chosen arbitrarily or by trial-and-error. Therefore there is a need for a systematic procedure to generate operational policy that guarantees the target CSD can be achieved. Furthermore, to predict the desired crystal morphology by means of model-based approaches, appropriate models covering the effects of the various operational parameters on the behavior of the crystals are necessary. That is, generic multi-dimensional model-based framework that covers a wide range of crystallization models and operational scenarios. The objectives of this work are to develop a systematic procedure for generating operational policies to achieve target CSD for batch cooling crystallization. In this procedure, an analytical CSD estimator will be employed to generate an operational policy. The estimator is based on the assumptions of constant supersaturation and an operation that is dominated by size dependent growth. The generated operational policy provides the supersaturation set point and by maintaining the operation at this point, a target CSD is achieved. Different operational policies that yield the same target CSD are then generated and compared with the CSD performance. All the operational policies generated by analytical CSD estimators are in this way validated with closed loop control. Here the generic multi-dimensional model-based framework for batch cooling crystallization has been developed and integrated with the monitoring and control procedure. Through this generic multi-dimensional model-based framework, a “specific” model can be generated and be used for closed loop control to verify the operation policies. Finally the performance between simulation models and analytical estimators will be compared and the best performance will be analyzed in term of CSD obtained, mean size diameter and total crystal mass. In this paper, the application of systematic procedure is illustrated for the potassium dichromate case study