## 4 Optimization Method  
The net power output is the objective function. Optimization variables include HTL evaporation temperature (\(T_{3}\)), HTL condensation temperature (\(T_{1}\)), and HTL superheat degree (\(T_{5}\)-\(T_{4}\)). The optimization ranges are listed in Table 4. The lower limit of \(T_{3}\) is \(T_{1}\) plus 5 K. The difference between working fluid critical temperature and the upper limit of \(T_{3}\) must be 15 K to prevent the HTL working fluid from approaching the critical point. The lower limit of \(T_{1}\) is HTL working fluid saturated temperature at the pressure of 101 kPa to avoid the contamination of non-condensable gases [29; 30; 43]; the upper limit is the LTL working fluid critical temperature minus 15 K and then plus \(\Delta T_{\rm pp2}\) to avoid the LTL working fluid from approaching the critical point. The lower limit of HTL superheat degree is 0 K, and the upper limit is \(T_{\rm g1}\) minus \(\Delta T_{\rm pp1}\) and \(T_{4}\) to avoid the heat transfer temperature of the HTL evaporator from being lower than \(\Delta T_{\rm pp1}\). In addition, the exhaust gas temperature at the exit of the HTL evaporator must be higher than the acid dew point temperature, which is 373.15 K [42]. By contrast, the LTL evaporation temperature must be lower than the LTL working fluid critical temperature minus 15 K.  
The genetic algorithm is a random search method based on the evolution rule of organisms; this algorithm was invented by Prof. [PERSON] in 1975 on the basis of the ideas of natural selection. The genetic algorithm mainly includes selection, crossover, and mutation calculations. Selection calculation selects good individuals from the generation. Crossover calculation is used for individuals to generate new individuals. Mutation calculation is adopted to avoid a local optimal solution. In the genetic algorithm, a population of candidate solutions to an optimization problem is evolved toward improved solutions. When the genetic algorithm is used, a population of randomly generated individuals is selected as the initial population, and iterative calculation begins. The population in each iteration is called a generation. The fitness of every individual in the population is calculated in each generation. If it does not satisfy the stopping criteria, then the more fit individuals are randomly selected from the population, and each individual is modified by crossover and mutation operators for creating a new generation. The new generation of candidate solutions is used for net iteration. When an appropriate fitness level is reached or the maximal generations are formed, the calculation is \begin{table}
\begin{tabular}{c c c} \hline
**Optimization Parameters** & **Lower Limit** & **Upper Limit** \\ \hline HTL evaporation temperature & HTL condensation temperature + 5 K & HTL working fluid critical temperature \(-15\)K \\ HTL condensation temperature & HTL working fluid normal boiling point & LTL working fluid critical temperature \(-15\)K + \(\Delta T_{\rm pp2}\) \\ HTL superheat degree & 0 K & \(T_{\rm g1}-\Delta T_{\rm pp1}-T_{4}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Optimization ranges of the operating parameters.